Boost Non-IID CIFAR-10 Accuracy: FedProx vs FedAvg in FLARE

Federated learning brings the promise of training models across decentralized devices while keeping data private, but engineers often hit practical roadblocks when moving from notebook experiments to production‑ready pipelines. The most common pain points include uneven data distribution across sites, confusing hyper‑parameter tuning for local epochs and regularization, device‑agnostic code that fails on CPU‑only environments, and missing or inconsistent logging that makes it hard to compare rounds. A solid solution starts with a clear data partitioning strategy: using a Dirichlet allocation lets you simulate realistic non‑IID splits while keeping the split reproducible by fixing the random seed. Next, wrap the training loop in a function that accepts the global model state, applies FedProx proximal term only when mu > 0, and returns a flattened parameter dictionary—this keeps the client code clean and makes it easy to swap optimizers or loss functions. Device handling should be centralized at the start: detect CUDA availability once, move both model and data to the chosen device, and avoid repeated .to(device) calls inside the inner loop to save overhead. Logging is another area where teams lose visibility; write a simple CSV logger that appends round number and test accuracy after each global aggregation, and also store the number of local steps taken for debugging. Finally, keep all paths configurable via command‑line arguments or a small config file so the same script can run on a local testbed, a Kubernetes cluster, or an edge gateway without code changes. By addressing data split realism, modular training logic, explicit device management, and structured logging, teams can move from fragile prototypes to scalable federated learning systems that are easier to debug, tune, and reproduce in real‑world settings. #AI #Product #MachineLearning #FederatedLearning #DeepLearning #PyTorch