Federated Learning: 5 Use Cases & Real Life Examples ['25]

McKinsey highlights inaccuracy, cybersecurity threats, and intellectual property infringement as the most significant risks of generative AI adoption.¹ Federated learning addresses these challenges by enhancing accuracy, strengthening security, and protecting IP, all while keeping data private.

By enabling real-time learning from decentralized sources, federated learning helps minimize breach risks, preserve proprietary information, and ensure a secure, privacy-first AI training approach for enterprises.

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

What is federated learning?

Federated learning is a decentralized machine learning approach that allows multiple organizations or devices to train machine learning models collaboratively without sharing private data. Instead of transferring raw data to a central server, only model updates or model parameters are exchanged, ensuring data privacy and data security.

By keeping training data localized and only aggregating insights, federated learning enhances data privacy while still leveraging distributed data for improved model accuracy.

How does federated learning work?

In machine learning, there are 2 steps, training and inference.

During 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 allow learnings (but not training data) from different sites to be securely collected. 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.²

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

Why is it important now?

Accurate machine learning models are valuable to companies and traditional centralized machine learning approaches have shortcomings like lack of continual learning on edge devices and aggregating 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 in the end-user device but then continual learning becomes a challenge since models are trained on a complete data set and the end user device does not have access to the complete dataset.

Another challenge with traditional machine learning is that user’s data gets aggregated in a central location for machine learning training which may be against the privacy policies of certain countries and may make the data more vulnerable to data breaches

Federated learning overcomes these challenges by enabling continual learning on end-user devices while ensuring that end user data does not leave end-user devices.

What are use cases and examples of federated learning?

Federated learning can be applied to mobile AI, healthcare, autonomous vehicles, smart manufacturing, and robotics, enabling privacy-preserving and decentralized model training.

It enhances on-device AI, medical AI collaboration, real-time AV decision-making, predictive maintenance, and swarm robotics, while addressing data security, bandwidth limitations, and regulatory compliance:

1. Mobile applications

Mobile apps use machine learning 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 models, 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.³

2. Healthcare

Federated learning benefits healthcare and healthacre 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 managing 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:

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.⁴

3. 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 without moving raw data.

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.⁵

4. 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 applications such as real-time quality control, energy efficiency optimization, and environmental monitoring, including air quality predictions for PM2.5 detection in smart cities.

5. 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 automatic controller design, which is particularly important 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 increased generalization to diverse environments and real robots, whereas baseline methods struggle with 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.⁶

What are the 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 models of federated learning.

What are 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:

Gossip learning

Gossip learning is a decentralized method where nodes/devices share model updates with a subset of their peers rather than relying on a central server. This peer-to-peer approach can enhance scalability. Studies have shown that gossip learning can outperform federated learning, especially when training data is uniformly distributed across nodes.⁷

Secure Multi-Party Computation (SMPC)

SMPC allows multiple parties to collaboratively compute a function over their inputs while keeping those inputs private. In the context of machine learning, SMPC can enable model training without revealing individual datasets, providing strong privacy guarantees.

However, SMPC can be computationally intensive and may require specialized protocols.

Homomorphic encryption

Homomorphic encryption enables computations to be performed directly on encrypted data, producing encrypted results that, when decrypted, match the outcome of operations performed on the plaintext.

While offering advanced privacy, homomorphic encryption is often computationally demanding, which can limit its practicality for large-scale machine learning tasks.

Differential privacy

Differential privacy involves adding carefully calibrated noise to data or computations to protect individual data points from being re-identified. In machine learning, this can mean adding noise to model updates or outputs, balancing privacy with model accuracy.

Decentralized federated learning

Unlike traditional federated learning, which relies on a central server, decentralized federated learning allows nodes to coordinate among themselves to train a global model.

This approach can mitigate single points of failure and enhance data privacy. However, it may face challenges related to network topology and communication overhead.

Proxy-based federated learning (ProxyFL)

In ProxyFL, each participant maintains two models: a private model and a publicly shared proxy model. The proxy models are shared among participants without a central server, facilitating collaborative learning while preserving data privacy.

This method also supports model heterogeneity, allowing participants to use different model architectures.

Conclusion

Federated learning represents a significant evolution in machine learning, addressing critical concerns regarding data privacy, security, and compliance. As industries increasingly rely on AI-driven insights, the ability to train models collaboratively without moving sensitive data offers a compelling advantage.

From healthcare and autonomous vehicles to mobile applications and smart manufacturing, federated learning enables organizations to leverage diverse datasets without compromising confidentiality or regulatory compliance.

Despite its promise, federated learning also presents challenges, including infrastructure demands, privacy vulnerabilities through model inversion, and limitations stemming from data heterogeneity.

However, ongoing research and complementary approaches—such as gossip learning, differential privacy, and decentralized frameworks—are rapidly advancing the field. As these innovations mature, federated learning is poised to play a central role in enabling the development of ethical, secure, and scalable AI across various sectors.

External Links

• 1. https://www.mckinsey.com/~/media/mckinsey/business%20functions/quantumblack/our%20insights/the%20state%20of%20ai%20in%202023%20generative%20ais%20breakout%20year/the-state-of-ai-in-2023-generative-ais-breakout-year_vf.pdf
• 2. Federated Learning powered by NVIDIA Clara | NVIDIA Technical Blog. NVIDIA Technical Blog
• 3. Learn how Google improves speech models - Google Assistant Help.
• 4. Announcing MedPerf Open Benchmarking Platform for Medical AI - MLCommons.
• 5. Federated Learning in Autonomous Vehicles Using Cross-Border Training | NVIDIA Technical Blog. NVIDIA Technical Blog
• 6. [2202.01141] Federated Reinforcement Learning for Collective Navigation of Robotic Swarms.
• 7. Gossip Learning as a Decentralized Alternative to Federated Learning - Inria - Institut national de recherche en sciences et technologies du numérique.