Federated Learning: 7 Use Cases & Examples

According to recent McKinsey analyses, the most pressing risks of AI adoption include model hallucinations, data provenance and authenticity, regulatory non-compliance, and AI supply chain vulnerabilities.¹

Federated learning (FL) has emerged as a foundational technique for organizations seeking to mitigate these risks. It allows models to learn from decentralized data while keeping sensitive information private and compliant with data localization and privacy laws.

Explore what federated learning is, how it works, common use cases with real-life examples, potential challenges, and its alternatives.

Use cases and examples of federated learning

Federated learning supports a wide range of AI systems where data sensitivity, decentralization, and real-time adaptation are critical. It is increasingly applied in agentic AI, finance, mobile applications, healthcare, autonomous transportation, smart manufacturing, and robotics, enabling collaborative model training:

1. Agentic AI

Rather than collecting data in a single shared pool, federated learning allows each agent to learn directly from its own interactions or environment. The agent then contributes only privacy-preserving model updates to a shared learning process, without exposing raw data.

This approach allows agents to continuously improve by learning from collective experience while still respecting privacy, data ownership, and regulatory requirements.

As a result, agentic AI can remain personalized and adaptive while remaining privacy-aware, making federated learning especially well-suited for sensitive settings where agents need to operate independently but still benefit from patterns observed across users, devices, or organizations.

Real-life example:

The rapid growth of IoT devices has enabled advances in areas such as healthcare, smart cities, and industrial systems, but it has also increased exposure to cyberattacks and privacy risks.

Traditional centralized intrusion detection systems rely on aggregating sensitive data, which creates communication overhead, privacy concerns, and single points of failure. To overcome these limitations, a recent study proposes a privacy-preserving IoT intrusion detection framework that combines Federated Learning (FL) with Agentic Artificial Intelligence.

FL enables decentralized model training, while Agentic AI adds adaptive, self-learning, and autonomous decision-making capabilities to respond to evolving threats.

The framework uses local anomaly detection, secure aggregation, and lightweight communication to balance accuracy and privacy, with agentic components optimizing defenses in real time.²

2. Finance applications

Federated learning enables financial institutions to collaboratively train AI models without sharing raw data, allowing each organization to keep sensitive information local while contributing to a stronger shared model.

This is especially valuable for fraud and financial crime detection, where threats span multiple banks and regions but data-sharing is restricted by regulations like GDPR and the EU AI Act.

Real-life example:

A recent article examines federated learning (FL) as a promising solution to enhance security and privacy in modern financial systems, particularly as digital finance and IoT-enabled endpoints, such as ATMs and POS devices, generate large volumes of sensitive data.

The article classifies FL use cases by regulatory exposure, from lower-risk applications such as portfolio optimization to high-risk tasks like real-time fraud detection, and highlights recent successes in fraud prevention and blockchain-integrated frameworks.

While FL offers clear benefits in terms of privacy, compliance, and scalability, the paper also underscores ongoing challenges, including data heterogeneity, adversarial attacks, interpretability, and regulatory integration.

For the future of FL in finance, the article identifies the combination of FL with technologies such as blockchain, differential privacy, secure multi-party computation, and quantum-secure methods as key to realizing trustworthy, future-proof AI systems.³

Real-life example:

Flower’s federated learning platform helps financial institutions collaboratively train AI models on decentralized data, thereby improving privacy, security, compliance, and predictive accuracy for tasks such as fraud detection, risk assessment, and other analytics.

Banking Circle, a global payments bank processing a significant share of Europe’s eCommerce flows, uses AI to manage its anti-money laundering (AML) operations by automatically flagging suspicious transactions for review.

As it expanded into the US, differences in transaction patterns and strict data-transfer constraints limited the effectiveness of models trained solely on European data. To address this, Banking Circle adopted Flower’s federated learning platform, enabling the company to train AML models across regions without moving sensitive data across borders.

This approach allowed the US model to learn from European insights while remaining locally compliant, with improvements feeding back into the European system over time.⁴

3. Mobile applications

Mobile apps use machine learning systems for personalization, like next-word prediction, face detection, and voice recognition. However, traditional AI training centralizes user data, which would increase concerns about privacy, security, and data governance. Federated learning addresses these challenges by allowing models to be trained across a network of devices without transmitting raw user data.

Here are some of the advantages of federated learning for mobile applications:

• Privacy-preserving AI: Sensitive user data remains on the device, reducing risks of data exposure while still improving model accuracy.
• Personalized and adaptive models: Apps can fine-tune AI models based on individual usage patterns without needing constant cloud updates.
• Lower bandwidth usage: Instead of uploading large datasets, only model updates are shared, making federated learning efficient for mobile networks.
• Improved security: By keeping data decentralized, federated learning mitigates risks associated with centralized data storage and breaches.

This approach is already being used in smartphone keyboards for predictive text and autocorrect, in voice assistants for speech recognition, and in biometric authentication for face and fingerprint recognition.

Real-life example:

Google employs federated learning to enhance on-device machine learning systems, such as the “Hey Google” detection in Google Assistant, enabling users to issue voice commands. This approach allows the training of speech models directly on users’ devices without transmitting audio data to Google’s servers, thereby preserving user privacy.

Federated learning facilitates the improvement of voice recognition capabilities by processing data locally, ensuring that personal audio information remains on the device.⁵

4. Healthcare

Federated learning benefits healthcare and health insurance by enabling powerful AI training while keeping patient data private.

Traditional data centralization, where hospitals and institutions pool medical records into a single repository, raises significant concerns about data governance, security, and compliance with regulations like HIPAA and GDPR.

Federated learning helps manage these issues by enabling collaborative model training across multiple institutions without requiring direct data sharing.

This approach provides several advantages:

• Enhanced privacy and security: Sensitive patient data remains within its original source, reducing the risks of exposure and data breaches.
• Improved data diversity: By training on datasets from different hospitals, research centers, and electronic health records, federated learning enables models to recognize rare diseases and improve diagnostic accuracy across diverse populations.
• Scalable medical AI: Machine learning models can be continuously refined on real-world data from multiple institutions, leading to more reliable predictive analytics and better patient outcomes.

Real-life example:

The MELLODDY project (Machine Learning Ledger Orchestration for Drug Discovery) is a European research initiative funded by the Innovative Medicines Initiative (IMI). The project brought together 10 pharmaceutical companies, academic, and technology partners to demonstrate how federated learning can accelerate drug discovery without sharing confidential data.

Rather than pooling proprietary datasets, which companies consider highly sensitive, MELLODDY developed a privacy-preserving federated machine learning platform that keeps each company’s data behind its own firewall and shares only model updates, not raw data, for collaborative learning.

This platform uses technologies such as AWS infrastructure, Kubernetes orchestration, and a private blockchain ledger to ensure secure and traceable model training across partners while protecting data ownership and intellectual property rights.

By exposing machine learning algorithms to vastly more data than any single company has, MELLODDY has demonstrated improved predictive performance and greater model applicability for predicting the biological activity and toxicology of drug candidates.⁶

Real-life example:

Owkin, a biotech company, uses federated learning to train AI models across multiple medical and research institutions without centralizing sensitive data.

Rather than collecting all patient data in one place, Owkin’s approach keeps the data where it’s stored (e.g., in hospital servers) and moves the machine learning algorithms to the data.

The models train locally on each partner’s dataset, and only model updates are shared back and aggregated to build a global model. This enables researchers and clinicians to benefit from a more diverse dataset than any single institution could provide, thereby improving the performance of predictive algorithms while still preserving patient privacy and data sovereignty.

Owkin positions this technique as particularly powerful for collaborative healthcare AI (like predicting treatment outcomes) and as a means to scale precision medicine without compromising privacy.⁷

Real-life example:

A growing push for federated learning in medical AI has led to initiatives like MedPerf, an open-source platform developed by a coalition of industry and academic partners.

MedPerf focuses on federated evaluation of AI models, ensuring they perform effectively on diverse, real-world medical data while maintaining patient confidentiality. By combining technical innovations in federated learning with governance frameworks that establish clinically relevant benchmarks, these initiatives aim to drive the adoption of AI in healthcare without compromising trust or security.

Figure 2: An example of federated learning in healthcare from MedPerf federated AI benchmarking framework.⁸

5. Transportation: Autonomous vehicles

Self-driving cars rely on a combination of advanced machine learning techniques to navigate complex environments.

Computer vision allows them to detect obstacles, while adaptive learning models help adjust driving behavior based on conditions like traffic or rough terrain.

However, traditional cloud-based approaches can introduce latency and pose safety risks, particularly in high-density traffic scenarios where split-second decisions are critical.

Federated learning offers a solution by decentralizing data processing and enabling real-time learning across multiple vehicles. Instead of relying solely on cloud-based updates, autonomous vehicles can collaboratively train models while keeping data localized. This approach ensures that vehicles continuously refine their decision-making based on the latest road conditions, without excessive data transfer.

By leveraging federated learning, self-driving cars can achieve three key objectives:

• Real-time traffic and road awareness: Vehicles can quickly process and share insights on road hazards, construction zones, or sudden weather changes, ensuring safer navigation.
• Immediate decision-making: Onboard AI can react faster to dynamic driving conditions, reducing dependency on remote servers and minimizing latency in critical moments.
• Continual model improvement: As more vehicles contribute their localized learnings, autonomous systems evolve and enhance their predictive accuracy over time.

By integrating federated learning, autonomous vehicles can not only enhance their immediate responsiveness but also create a collective intelligence that improves the overall safety and efficiency of self-driving systems.

Real-life example:

NVIDIA’s AV Federated Learning platform, powered by NVIDIA FLARE, enables autonomous vehicle (AV) models to be trained collaboratively across different countries while preserving data privacy and complying with regional regulations like GDPR and PIPL.

Instead of centralized training, which can be costly and restricted by data transfer laws, federated learning allows models to be trained locally on country-specific data, improving global model performance.

The platform integrates with existing machine learning systems and operates with a central server on AWS in Japan, supporting cross-border training. Since launch, it has produced over a dozen AV models, with performance matching or exceeding locally trained counterparts, and adoption has grown from 2 to 30 data scientists within a year.⁹

6. Smart manufacturing: Predictive maintenance

As Industry 4.0 advances, AI-driven predictive maintenance helps manufacturers reduce downtime, extend equipment lifespan, and boost efficiency. However, its implementation faces challenges, including data privacy, security, and cross-border sharing restrictions.

Federated learning addresses these issues by enabling manufacturers to develop predictive maintenance models without transferring sensitive industrial data. Instead of aggregating information from multiple plants or customers into a central repository, federated learning allows each site to train models locally. These models then contribute insights to a global predictive system without exposing proprietary data.

Key benefits of federated learning for predictive maintenance include:

• Privacy-preserving AI: Industrial data remains on-site, eliminating concerns about sharing proprietary or sensitive operational data with external entities.
• Cross-border compliance: Many manufacturers operate in multiple countries, each with different data protection regulations. Federated learning enables compliance by keeping data localized while still benefiting from collective intelligence.
• Adaptability to diverse equipment and conditions: Manufacturing environments vary widely based on machinery, workload, and operational settings. Federated learning allows predictive models to be tailored to local conditions while contributing to a broader understanding of equipment failure patterns.

Beyond predictive maintenance, federated learning is also being applied in smart manufacturing for real-time quality control, energy efficiency optimization, and environmental monitoring, including air-quality predictions for PM2.5 detection in smart cities.

7. Robotics

Robotics depends on machine learning for perception, decision-making, and control, from simple tasks to complex navigation. As applications grow, continuous learning and adaptability are essential, but centralized training faces data transfer, privacy, and communication challenges, especially in multi-robot systems.

Federated learning enables robots to improve their models collaboratively while keeping data localized. This decentralized approach is particularly useful for multi-robot navigation, where communication bandwidth limitations can be a challenge.

Instead of relying on constant data transmission to a central server, federated learning allows robots to train on their local experiences and share only essential model updates, optimizing learning efficiency without overwhelming network resources.

Here are the key benefits of federated learning in robotics:

• Decentralized learning for improved autonomy: Robots can refine their perception and control models locally, reducing reliance on cloud-based updates and enabling faster adaptation to new environments.
• Efficient multi-robot collaboration: Groups of robots can exchange learned experiences without excessive data transfer, which would make federated learning ideal for fleet management, warehouse automation, and swarm robotics.
• Enhanced privacy and security: Sensitive operational data remains within each robotic system, mitigating concerns about data exposure in industrial or military applications.
• Scalability across diverse environments: Robots operating in different locations, such as factories, hospitals, or urban areas, can contribute insights to a global model while still adapting to their specific surroundings.

Real-life example:

Recent advancements in Deep Reinforcement Learning (DRL) have enhanced robotics by enabling automated controller design, particularly for swarm robotic systems. These systems require more sophisticated controllers than single-robot setups to achieve coordinated collective behavior.

While DRL-based controller design has proven effective, its reliance on a central training server poses challenges in real-world environments with unstable or limited communication.

To address this, a recent article introduced FLDDPG, a novel Federated Learning (FL)-based DRL training strategy tailored for swarm robotics.

Comparative evaluations under limited communication bandwidth demonstrate that FLDDPG offers improved generalization across diverse environments and real robots, whereas baseline methods struggle under bandwidth constraints.

The findings suggest that federated learning enhances multi-robot navigation in environments with restricted communication bandwidth, addressing a key challenge in real-world, learning-based robotic applications.¹⁰

Summarize This Research

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What is federated learning?

Federated learning is a collaborative machine learning paradigm where multiple participants train models using local data and only share model updates or computed information, while raw data remains on-site. Most practical FL systems still use a central aggregator to orchestrate training rounds.

Instead of transferring raw training data, participants send model updates or gradients for aggregation. However, sharing updates alone does not guarantee privacy without additional techniques such as secure aggregation, differential privacy, or cryptographic protections.

By keeping training data local and aggregating insights, federated learning enhances data privacy while still leveraging distributed data to improve model accuracy.

How does federated learning work?

In machine learning, there are two steps: training and inference.

During the training step:

1. Local machine learning (ML) models are trained on local heterogeneous datasets. For example, as users use a machine learning application, they spot mistakes in the machine learning application’s predictions and correct those mistakes. These create local training datasets in each user’s device.
2. The parameters of the models are exchanged between these local data centers periodically. In many models, these parameters are encrypted before exchanging. Local data samples are not shared. This improves data protection and cybersecurity.
3. A shared global model is built.
4. The characteristics of the global model are shared with local data centers to integrate the global model into their ML local models.

For example, Nvidia’s Clara solution includes federated learning. Clara and Nvidia EGX enable learning through the secure collection of model updates (but not training data) from different sites. This helps models to set up a global model while preserving data privacy (See Figure below).

Figure 1: An example from NVIDIA demonstrating how federated learning works.¹¹

In the inference step, the model is stored on the user device, so predictions are quickly prepared using the model on the user device.

Distributed training in federated learning

Federated learning and distributed training are distinct concepts: federated learning refers to collaborative training with decentralized data, while distributed training (parallel computation across nodes within one participant) is a local optimization strategy and not inherent to FL itself.

In federated learning, clients, such as hospitals, mobile devices, or organizations, independently train models on their local data and share only the model updates with a central aggregator.

Some clients may have access to multiple GPUs, servers, or edge nodes. These resources can be used in parallel to accelerate or scale up local training. This setup creates a hierarchy:

• At the top level, multiple clients participate in federated learning.
• At the local level, each client may use distributed training across its available infrastructure.

Local distributed training can follow:

• Data parallelism: Each worker holds a replica of the model and trains on a subset of the local data.
• Model parallelism: The model is partitioned across workers, which is helpful for large models that do not fit in a single device’s memory.

Key benefits of combining distributed training with federated learning

1. Improved scalability

Clients with large datasets or computationally intensive models may struggle to complete training efficiently on a single machine.

Distributed training enables the client to utilize multiple nodes or devices, thereby improving throughput and supporting larger workloads.

2. Efficient resource utilization

Organizations often have local clusters or idle compute resources. Using distributed training within federated learning enables them to utilize these resources fully without data centralization.

3. Faster local training

Distributing computation reduces the wall-clock time for local model updates. This can shorten each federated learning round and reduce overall training time across clients.

4. Separation of concerns

Federated training and local distributed training operate independently of each other. The federated server does not need to manage internal scheduling or coordination of client resources. This modular design simplifies both deployment and maintenance.

5. Flexible system design

Different clients can choose different local training configurations based on their compute environments. Some may use single-node training, while others use distributed setups. The federated protocol remains unchanged.

Why is it important now?

Accurate machine learning models are valuable to companies, but traditional centralized machine learning systems have shortcomings, such as a lack of continual learning on edge devices and the aggregation of private data on central servers. These are alleviated by federated learning.

In traditional machine learning, a central ML model is built using all available training data in a centralized environment. This works without any issues when a central server can serve the predictions.

However, in mobile computing, users demand fast responses, and the communication time between the user device and a central server may be too slow for a good user experience. To overcome this, the model may be placed on the end-user device, but then continual learning becomes challenging because models are trained on a complete dataset, and the end-user device does not have access to it.

Another challenge with traditional machine learning is that users’ data is aggregated in a central location for training, which may violate the privacy policies of specific countries and make the data more vulnerable to breaches.

Federated learning overcomes these challenges by enabling continual learning through local data on end-user devices, while ensuring that user data remains on the device.

Recently, federated learning has also become a cornerstone of federated fine-tuning, where enterprises adapt foundation models (such as Llama 3, Mistral, or Gemini) to private data without exposing the data itself.

Challenges of federated learning

Investment requirements

Federated learning models may require frequent communication between nodes. This means storage capacity and high bandwidth are among the system requirements.

Data privacy

• Data privacy is an important issue, as it is not collected on a single entity/server in federated learning; there are multiple devices for collecting and analyzing data. This can increase the attack surface.
• Even though only models, not raw data, are communicated to the central server, models can possibly be reverse-engineered to identify client data.

Performance limitations

• Data heterogeneity: Models from diverse devices are merged to build a better model in federated learning. Device-specific characteristics may limit the generalization of the models from some devices and may reduce the accuracy of the next version of the model.
• Indirect information leakage: Researchers have considered situations where one of the members of the federation can maliciously attack others by inserting hidden backdoors into the joint global model.
• Federated learning is a relatively new machine learning procedure. New studies and research are required to improve its performance.

Centralization

There is still a degree of centralization in federated learning where a central model uses the output of other devices to build a new model. Researchers propose using blockchained federated learning (BlockFL) and other approaches to build zero-trust federated learning models.

What are the alternatives for federated learning?

While federated learning offers privacy benefits, several alternative approaches and frameworks have been developed to address its limitations and adapt to various scenarios. Here are some alternatives:

Centralised or traditional machine learning

In a centralised machine learning system, all data from different sources is collected and stored in a single location, such as a cloud server or a company data center. The model is then trained using this combined dataset.

Key characteristics:

• The model has direct access to all available data.
• Data preprocessing and model training occur on a central server.
• Clients or data owners transfer their data to the central system for analysis.

Advantages:

• The training process is more straightforward to manage and monitor.
• Data consistency is easily maintained because all records are in one place.
• Model performance can benefit from complete access to all data variations.

Limitations:

• Privacy and compliance issues can arise when data transfer is restricted by law or company policy.
• A single point of failure can bring the entire system down if the server experiences downtime or a security breach.
• Transferring large datasets can increase bandwidth usage and processing costs.

This approach is best suited when privacy is not a significant concern, and all data can be safely centralized without regulatory conflicts.

Secure multi-party computation

Secure multi-party computation (SMPC) is a cryptographic technique that enables multiple parties to compute a shared function without revealing their individual datasets. Each participant encrypts their data, and the computation proceeds in a way that shows only the final model output.

Key characteristics:

• Parties collaborate to train a model while keeping raw data private.
• Cryptographic techniques such as secret sharing and homomorphic encryption are commonly used.
• No single participant has access to the complete dataset.

Advantages:

• Protects sensitive data throughout the entire training process.
• Allows organizations to cooperate on model development even when data cannot be shared.
• Enhances compliance with privacy regulations, such as GDPR.

Limitations:

• Computational requirements are high due to cryptographic operations.
• Communication among parties can be slow, which can affect scalability.
• Implementation complexity increases with the number of participants.

SMPC is appropriate in situations where strong privacy requirements exist and a secure computation infrastructure is available.

Differential privacy

Differential privacy (DP) ensures that no single data point in a dataset can be distinguished or inferred after analysis. It achieves this by introducing controlled randomness, often in the form of noise, to the training data or model updates.

Key characteristics:

• Privacy is mathematically quantified using a parameter called epsilon (ε).
• The method protects individuals’ data contributions even when the overall dataset is shared.
• It can be applied to both centralized and distributed systems.

Advantages:

• Offers a measurable level of privacy protection.
• It can be combined with other learning techniques, such as federated learning.
• Limits the risk of data re-identification.

Limitations:

• Excessive noise can reduce model accuracy.
• Selecting the right privacy budget (ε) requires careful tuning.
• Does not, by itself, address distributed coordination or computation.

Differential privacy is suitable for organizations that need a balance between data utility and privacy protection.

Gossip or peer-to-peer learning

Gossip learning, also known as peer-to-peer learning, removes the need for a central server. Each node or client trains a local model and shares updates directly with neighboring nodes. Over time, these updates spread through the network, and the models converge.

Key characteristics:

• Nodes communicate locally with peers rather than a central aggregator.
• Model parameters or gradients are exchanged in a decentralized fashion.
• Learning occurs asynchronously across the network.

Advantages:

• No single point of failure since there is no central coordinator.
• Can function effectively in dynamic networks, such as IoT or edge environments.
• Reduces reliance on a trusted central entity.

Limitations:

• Communication overhead may increase due to random peer exchanges.
• Convergence can be slower compared to centralized aggregation.
• Monitoring and control are more difficult in fully decentralized systems.

This approach is effective for distributed systems where a central server cannot be maintained or trusted.

Split learning

Split learning divides a machine learning model into two or more segments. The first segment is trained on the client device using local data, and its output (activations) is sent to a server, which completes the remaining training.

Key characteristics:

• The model is partitioned between clients and a central server.
• Clients never share raw data; only intermediate outputs are transmitted.
• The system requires coordination between the client and server during training.

Advantages:

• Reduces computational demands on clients by training only part of the model.
• Provides a degree of data privacy as raw data remains local.
• Can integrate with existing cloud infrastructure.

Limitations:

• Intermediate activations may still reveal some data information if intercepted.
• Requires stable communication between client and server.
• Implementation complexity increases for deep or multi-layered models.

Split learning is suitable for environments with limited client resources or when privacy constraints prevent full data sharing.

Transfer learning and model distillation

Transfer learning and model distillation enable collaboration without direct data sharing. Each organization or device trains its own model locally, and then a central model learns from these individual models’ outputs or predictions rather than their internal parameters.

Key characteristics:

• Knowledge is transferred through predictions, not through full model weights.
• The global model is refined using the collective experience of all participants.
• Local models remain independent and can continue to specialize.

Advantages:

• Reduces communication volume by sharing only distilled information.
• Allows flexibility in model architectures across participants.
• Can achieve reasonable performance even with heterogeneous data sources.

Limitations:

• Some information loss occurs during the distillation process.
• Global model quality depends on the accuracy of local models.
• Lacks the coordinated synchronization of federated learning.

This method is practical when client data is highly diverse or when clients use different model types.

Hybrid or combined architectures

Hybrid systems merge elements from several privacy-preserving methods to address specific challenges. Examples include federated learning combined with differential privacy, secure multi-party computation, or hierarchical architectures in which regional aggregators communicate with a central server.

Key characteristics:

• Different layers or modules of the system use different privacy techniques.
• Can include regional or tiered aggregation for scalability.
• Often tailored to meet regulatory and performance requirements.

Advantages:

• Offers flexibility in balancing privacy, accuracy, and computational cost.
• Can handle large-scale or geographically distributed data sources.
• Enables organizations to customize architectures for specific constraints.

Limitations:

• Implementation is complex due to interactions among multiple components.
• System maintenance and debugging require advanced expertise.
• Communication protocols can become intricate and resource-intensive.

Hybrid approaches are practical for large organizations that need to manage multiple datasets under varying legal and technical conditions.

Reference Links

1.

The State of AI: Global Survey 2025 | McKinsey

McKinsey & Company

2.

ResearchGate - Temporarily Unavailable

3.

https://arxiv.org/pdf/2510.14991

4.

Federated AI in Finance

5.

https://support.google.com/assistant/answer/11140942?hl=en&visit_id=638754743065811169-1556460732&rd=1

6.

MELLODDY | IHI Innovative Health Initiative

7.

What is federated learning? | Owkin

8.

Announcing MedPerf Open Benchmarking Platform for Medical AI - MLCommons

9.

Federated Learning in Autonomous Vehicles Using Cross-Border Training | NVIDIA Technical Blog

NVIDIA Developer

10.

[2202.01141] Federated Reinforcement Learning for Collective Navigation of Robotic Swarms

11.

Federated Learning powered by NVIDIA Clara | NVIDIA Technical Blog

NVIDIA Developer

Principal Analyst

Cem Dilmegani

Principal Analyst

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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 55% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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Sıla Ermut

Industry Analyst

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Sıla Ermut is an industry analyst at AIMultiple focused on email marketing and sales videos. She previously worked as a recruiter in project management and consulting firms. Sıla holds a Master of Science degree in Social Psychology and a Bachelor of Arts degree in International Relations.

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