Federated learning is supported through a combination of decentralized model training, privacy-preserving techniques, and infrastructure designed to handle communication between devices. In this approach, a central server coordinates the training process without directly accessing raw data. Instead, individual devices or servers (called clients) train local models on their data and share only model updates (e.g., gradients or parameters) with the server. The server aggregates these updates—typically by averaging them—to create an improved global model, which is then redistributed to clients for further training. This cycle repeats, enabling the global model to learn from diverse data sources while keeping raw data localized. For example, TensorFlow Federated (TFF) provides APIs for simulating federated training, while frameworks like Flower offer agnostic tools to integrate with existing PyTorch or TensorFlow workflows.
Privacy and security mechanisms are critical to support federated learning. Techniques like differential privacy add controlled noise to model updates to prevent reverse-engineering sensitive data from shared gradients. Secure aggregation protocols ensure that individual client updates remain encrypted until combined, so the server cannot view contributions from specific clients. For instance, Google uses secure aggregation in production systems like Gboard to train next-word prediction models without exposing user typing data. Homomorphic encryption is another method, allowing computations on encrypted model updates, though it is computationally intensive. Additionally, federated learning often incorporates access controls and audit logs to ensure compliance with regulations like GDPR, especially in healthcare or finance where data sensitivity is high.
The infrastructure for federated learning addresses challenges like communication efficiency and device heterogeneity. Since transmitting large model updates across networks can be costly, methods like model quantization (reducing numerical precision) or sparsification (sending only significant updates) minimize bandwidth usage. Federated averaging (FedAvg), a foundational algorithm, balances local training iterations on clients with periodic aggregation to reduce communication rounds. Tools like NVIDIA’s Clara for healthcare or OpenMined’s PySyft enable on-device training with resource constraints, optimizing for varying hardware capabilities. Furthermore, frameworks handle issues like stragglers (slow clients) by allowing asynchronous updates or partial participation in training rounds. For example, Android’s federated learning system dynamically selects a subset of active devices per training round to maintain efficiency while scaling to millions of users. This combination of algorithmic optimization, privacy safeguards, and infrastructure design ensures federated learning operates reliably across distributed environments.