Multi-Objective Federated Learning via Evolutionary Knowledge Transfer

Federated learning enables multiple clients to collaboratively train a global model without exchanging raw data, thereby alleviating data silos and privacy concerns. However, federated learning model design is still challenged by complex architecture and hyperparameter optimization, especially under heterogeneous data distributions. To address these issues, this paper proposes a multi-objective federated learning framework based on particle swarm optimization (PSO). Specifically, neural architecture search and training-parameter optimization are formulated as a multi-objective evolutionary optimization problem, where predictive performance and model complexity are optimized jointly. Furthermore, to improve robustness under non-independent and identically distributed (non-IID) data, a clustered multi-population extension is developed. In this framework, each cluster is associated with a dedicated particle population. An inter-population evolutionary knowledge transfer mechanism is then introduced to enable effective sharing of search experience across clusters, thereby improving the optimization efficiency and adaptability of federated learning under heterogeneous environments. Experiments on MNIST and CIFAR10 under both IID and non-IID settings demonstrate that the proposed methods achieve a superior trade-off between predictive accuracy and model complexity and outperform baseline approaches in robustness, search efficiency, and adaptability to imbalanced distributed environments.