解决异构环境下联邦学习的个性化与泛化矛盾

Federated learning enables collaborative model training across distributed entities while maintaining individual data privacy. A key challenge in federated learning is balancing the personalization of models for local clients with generalization for the global model. Recent efforts leverage logit-based knowledge aggregation and distillation to overcome these issues. However, due to the non-IID nature of data across diverse clients and the imbalance in the client's data distribution, directly aggregating the logits often produces biased knowledge that fails to apply to individual clients and obstructs the convergence of local training. To solve this issue, we propose a Hierarchical Knowledge Structuring (HKS) framework that formulates sample logits into a multi-granularity codebook to represent logits from personalized per-sample insights to globalized per-class knowledge. The unsupervised bottom-up clustering method is leveraged to enable the global server to provide multi-granularity responses to local clients. These responses allow local training to integrate supervised learning objectives with global generalization constraints, which results in more robust representations and improved knowledge sharing in subsequent training rounds. The proposed framework's effectiveness is validated across various benchmarks and model architectures.