Feed: Towards Personalization-Effective Federated Learning

Federated learning (FL) has become an emerging paradigm via cooperative training models among distributed clients without leaking data privacy. The performance degradation of F1 on heterogeneous data has driven the development of personalized FL (PFL) solutions, where different models are built for individual clients. However, existing PFL approaches often have limited personalization in terms of modeling capability and training strategy. In this paper, we propose a novel PFL solution, Feed, that employs an enhanced shared-private model architecture and equips with a hybrid federated training strategy. Specifically, to model heterogeneous data for different clients, we design an ensemble-based shared encoder that generates an ensemble of embeddings, and a private decoder that adaptively aggregates these embeddings for personalized prediction. In addition, we propose a server-side hybrid federated aggregation strategy to enable effective training of the heterogeneous shared-private model. To prevent personalization degradation in local model updates, we further optimize the personalized local training on the client-side by smoothing the historical encoders. Extensive experiments on MNIST/FEMNIST, CIFARIO/CIFARIOO, and YELP datasets demonstrate that Feed consistently outperforms state-of-the-art approaches.