Federated Learning: The Future of Privacy-Preserving Machine Learning

Amna ManzoorLast Updated on October 27, 2026

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Key Takeaways

Federated learning trains shared models on data that stays local, but it needs added safeguards such as differential privacy and secure aggregation to protect privacy.

Federated learning sends model updates to a central server instead of raw data, and the server combines them with Federated Averaging, weighting each update by the data behind it.
Model updates can still leak training data, and NIST concluded in 2024 that federated learning alone is not a complete privacy solution during training.
Differential privacy and secure aggregation are the usual added layers. Secure aggregation is an extra protocol, so not every federated learning system uses it.
Federated learning can help with GDPR and CCPA by minimizing the personal data used, but the UK ICO calls such technologies "not a silver bullet", and processing must still be lawful.
Google reported training and deploying more than twenty Gboard language models with federated learning, and Swift's 2024 bank fraud pilot trains on locally held datasets.
The main challenges are communication overhead, uneven data across devices, and security risks such as a malicious participant poisoning the global model with manipulated updates.

Teams in regulated sectors should have legal or data protection specialists review a federated learning setup before it goes live.

Federated learning trains a shared machine learning model across many devices or organizations while the raw data stays where it was created, and only model updates are sent to a central server.

Much everyday software learns from personal data. Smartphones predict text, fitness apps track steps, and voice assistants learn preferences. All of them raise the same privacy question: who gets to see the data behind the feature?

This post is for product and data leaders, and for engineers new to distributed ML, at companies handling sensitive data in healthcare, finance or consumer apps. If you are weighing machine learning solutions for that kind of data, it covers how federated learning works, where it helps and where it falls short.

Download this guide to discover essential techniques to protect sensitive data while accessing AI systems.

What is Federated Learning?

Federated Learning is a decentralized approach to machine learning that allows models to be trained across multiple devices (like smartphones, tablets, or computers) without moving the data from those devices. Instead of gathering all the data in a central server (as in traditional ML), federated learning brings the training process directly to where the data resides.

In simpler terms, think of it as teaching a class where every student (device) learns individually from their own materials (data), and then shares their knowledge (model updates) with the teacher (central server). The teacher gathers the insights, updates the overall understanding (global model), and sends it back to the students for further improvement; without ever taking the students' materials.

What is Federated Learning.png

Now let’s take a deeper look at each step.

How Does Federated Learning Work Step by Step?

Federated Learning is a distributed machine learning process that enables model training across multiple devices without transferring raw data to a central server. Instead, it focuses on collaborative learning while helping protect privacy and reducing data transfer costs. Here’s how the federated learning process works in detail:

1. Initial Model Distribution

The process starts with a global model initialized and stored on a central server. This global model is then distributed to a large number of edge devices, such as smartphones, computers, or IoT devices. Each of these devices has access to its own local data; data generated by the users or the operations of the device.

For example, a smartphone’s predictive text system receives an initial language model from a central server, ready to be improved using local user data on the device.

2. Local Model Training on Devices

Each device uses its local data to train a copy of the global model locally. During this training process, the device adjusts the model’s parameters to improve its performance based on the patterns identified from the local data.

a. Techniques Used: The most common training technique in this step is stochastic gradient descent (SGD). This technique iteratively adjusts the model parameters by minimizing the error between the predicted and actual outputs for the data on the device.

Example: In the case of the predictive text system, the device trains the model based on the user’s typing habits, learning patterns like commonly used words or phrases, without ever transferring the raw text data to a central server.

3. Sending Model Updates (Instead of Raw Data)

Once the local training is complete, the device does not send the raw data back to the central server. Instead, it sends model updates, which are the changes in the model parameters that occurred during local training. These updates reflect how the model changed on the device, and they can still reveal details about the data behind them. NIST's 2024 post "Privacy Attacks in Federated Learning" reports that it is often possible to extract raw training data from model updates.

Key Privacy Measures: To further protect privacy, many systems implement techniques such as:

a. Differential Privacy: Adds random noise to the model updates to ensure that even if someone tries to reverse-engineer the update, they won’t be able to identify sensitive details about the data.

b. Homomorphic Encryption: Encrypts the model updates before sending them, so even if the updates are intercepted, they remain unintelligible to attackers.

For instance, the predictive text system sends updated model weights (i.e., adjusted parameters) that reflect the device's learning. The weights do not contain the typed text directly, but they can still leak information about it, so the privacy measures above are a necessary layer of the system.

4. Aggregation of Updates at the Server

When the central server receives model updates from multiple devices, it aggregates them to create an improved global model. The server applies Federated Averaging (FedAvg), which calculates a weighted average of the received model updates. The weight given to each update is often proportional to the amount of data the device used during training, ensuring that devices with more data have a more significant influence on the global model.

When a secure aggregation protocol (a form of secure multi-party computation, or SMPC) is used, the server sees only the combined result and never an individual device's update. In Google's secure aggregation design, for example, the coordinating server can decrypt only the average update, and only after hundreds or thousands of users have taken part. Secure aggregation is an added protocol, and not every federated learning system uses it.

For example, the server aggregates the model updates from thousands of smartphones to improve the predictive text model, making it more accurate based on the collective learning from many users while preserving their privacy.

5. Global Model Distribution

After the aggregation, the central server generates an updated global model that incorporates the knowledge from all participating devices. This updated global model is then sent back to all the devices, replacing their local models.

As an example, the newly improved predictive text model, which has learned from the typing patterns of many users, is now sent back to all the smartphones in the network. This enables each device to benefit from the collective training without sacrificing user privacy.

6. Iterative Training (Ongoing Cycles)

Federated learning is an iterative process, meaning the steps from local training to global aggregation are repeated across several rounds. With each round, the global model becomes more refined and accurate. Devices continue to generate new local data over time, and each new cycle incorporates fresh updates into the global model.

a. Client Participation: Not all devices participate in every round. Federated learning systems often use partial client participation, where only a subset of available devices are selected for each training round. This helps reduce the communication load and improves scalability.

For example, over time, the predictive text model improves after several rounds of training as new user typing patterns are learned and incorporated into the global model. Each cycle refines the model to be more responsive and accurate for users.

How Do You Make Federated Learning Efficient and Reliable?

Reducing Communication Costs in Federated Learning

Every training round moves model updates between devices and the server, and those updates can be large. Across thousands of devices, this traffic becomes a bottleneck. Two common fixes are:

a. Sparse updates. Devices send updates only for the most important parts of the model, which shrinks each transfer.

b. Compression. Updates are compressed before upload. Google reported that compressing updates could cut upload communication costs by up to a further 100x. Compression can also discard useful information, so teams need to test how much compression their model can tolerate before accuracy drops.

Handling Device and Data Differences in Federated Learning

Participating devices differ in computing power, connectivity, and in how much data they hold and how good it is. Two issues come up most often:

a. Heterogeneous data. Data on one device can be skewed or far smaller than on another. Aggregation has to account for this so a few devices do not bias the global model.

b. Device failures. Some devices drop out of a round because of lost connections or hardware problems. The server aggregates updates from the devices that finish and moves on to the next round.

Why Does Federated Learning Matter for Privacy?

Federated learning matters for privacy because it lets teams train models on sensitive data without collecting that data in one place, which reduces exposure and the cost of moving data around.

Enhanced Privacy with Federated Learning

Raw data stays on the device or inside the organization that created it. That reduces how much personal data is collected centrally, which lowers exposure if a central system is breached. This matters most in healthcare, finance and education, where records such as medical histories or financial transactions are involved.

Keeping data local does not make a system private by default. NIST concluded in 2024 that federated learning alone is not a complete solution for protecting privacy during training, so teams still add safeguards such as differential privacy and secure aggregation.

It can help, but it does not deliver compliance by itself. The General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) set strict rules on how companies collect and store personal data. In its 2024 blog post "Data protection considerations for implementing federated learning", the UK Information Commissioner's Office (ICO) says federated learning can help an organization minimize the personal information it uses. The ICO also describes privacy-enhancing technologies like this as "not a silver bullet", because security and privacy risks can remain, and processing that uses federated learning must still be lawful, fair and transparent.

Teams in regulated sectors should have legal or data protection specialists review their federated learning setup before it goes live.

Reduced Data Transfer Costs with Federated Learning

Centralizing large volumes of data can be costly and time-consuming. By training models on local devices, federated learning reduces the amount of data that needs to be transferred across networks, saving both bandwidth and resources.

Personalization with Federated Learning

Federated learning also enables personalized machine learning models. For example, a language model on your smartphone can learn from your specific usage patterns and improve its accuracy without your raw typing data being collected centrally. Users get better predictions while their typing stays on the device.

Real-World Applications of Federated Learning

Federated learning is already in use across several industries. Here are a few examples:

Federated Learning in Healthcare

In healthcare, federated learning lets hospitals train ML models together while patient records stay inside each hospital's systems, which lowers exposure. For instance, hospitals can collaborate on models that predict patient outcomes without pooling their records.

The shared updates and the finished model can still reveal personal information, so hospitals add safeguards such as differential privacy and secure aggregation.

Federated Learning on Mobile Devices

Google uses federated learning in the Gboard keyboard on Android to improve typing predictions and autocorrections. Phones send model updates, not typed text. In a 2023 paper, "Federated Learning of Gboard Language Models with Differential Privacy", Google Research reported that it had trained and deployed more than twenty Gboard language models this way, and that all of Gboard's next-word-prediction neural network language models now have differential privacy guarantees.

Federated Learning in Finance

Banks are piloting federated learning to detect fraud using data held by several institutions, without pooling customer records. In 2024, Swift signed up major banks to a fraud prevention pilot in which an AI model is trained on multiple locally held datasets.

Federated Learning in Smart Homes

Federated learning is also being applied to smart home devices. By training models directly on individual devices like smart speakers or thermostats, companies can improve the functionality of these devices without storing sensitive household data in the cloud.

What Are the Challenges of Federated Learning?

The main challenges of federated learning are communication overhead, uneven data across devices, and security and privacy risks that remain even when raw data stays local.

1. Communication overhead. Each round adds network cost and delay, and devices with weak connections slow training down.

2. Heterogeneous data. Data varies in quality, quantity and format between devices, and the global model is only reliable if training accounts for those differences.

3. Security risks. Federated learning is still open to attack. A malicious participant could manipulate its model updates to poison the global model. Privacy also needs extra work: NIST's 2024 post concludes that federated learning alone is not a complete solution for protecting privacy during training, and the ICO notes that it does not stop personal information from appearing in the model's output. Differential privacy and secure aggregation are the usual added layers. Before deployment, teams should test the full federated learning pipeline for vulnerabilities, and automated testing services can cover that work.

The Future of Federated Learning

Federated learning is a rapidly evolving field, and advancements are continuously being made to improve its efficiency and security, often supported by specialized software development services that help teams build production implementations. As more organizations prioritize data privacy and comply with strict regulations, federated learning will likely become a critical component of machine learning frameworks.

Work continues on making federated learning cheaper to run and harder to attack, with privacy layers such as differential privacy, secure aggregation and homomorphic encryption. Differential privacy is already standard in production for Gboard's next-word-prediction models, as covered above. As the Internet of Things (IoT) continues to grow, federated learning will help smart, interconnected devices learn from data while that data stays on the device.

Conclusion

Federated learning fits best when data is sensitive or spread across devices or institutions that cannot pool it, such as hospitals building a shared outcome model or a keyboard app learning from many phones. It works when combined with differential privacy, secure aggregation and careful testing, and it should not be treated as a privacy guarantee on its own. Expect extra engineering effort for communication, uneven data and security reviews. Teams evaluating privacy-preserving ML can talk to Arbisoft about designing and building it.