Improving PID-Based Aggregation in Federated Learning for Consumer Applications

Consumer devices such as smartphones, wearables, and smart home assistants increasingly rely on machine learning models for personalized and secure services. Federated Learning (FL) enables decentralized training without sharing raw data by communicating model updates only, but faces challenges from heterogeneous clients and adversarial behavior that destabilize convergence. This paper introduces FedPIDAvg_tuned, a feedback-based FL aggregation that integrates a Proportional–Integral–Derivative (PID) controller with Bayesian Optimization to automatically stabilize the aggregation and improve robustness. Experiments on non-IID FEMNIST data demonstrate up to 4% higher accuracy and improved convergence stability compared to traditional FL aggregation, showing that adaptive control strengthens model robustness under adversarial conditions.