In Federated Learning (FL), model aggregation is pivotal. It involves a global server iteratively aggregating client local trained models in successive rounds without accessing private data. Traditional methods typically aggregate the local model from the current round alone. However, due to the statistical heterogeneity across clients, the local model from each client may be greatly diverse, making the obtained global model incapable of maintaining their specific knowledge. In this paper, we introduce a novel method, FedCDA, which selectively aggregates local models from various rounds, decreasing discrepancies between local models. The principle behind FedCDA is that the local model from each client may converge to distinct local optima over rounds due to the varied received global models and non-convex essences of deep neural networks, and each local model fits its local data well. Therefore, for each client, we select a local model from multiple rounds to minimize the divergence from other clients. This ensures the aggregated global model remains aligned with all selected local models to maintain their data knowledge. Extensive experiments conducted on various models and datasets reveal our approach outperforms state-of-the-art aggregation methods.
FedCDA: Federated Learning with Cross-rounds Divergence-aware Aggregation
Haozhao Wang Haoran Xu Yichen Li Yuan Xu Ruixuan Li Tianwei Zhang