FedCoSR: Personalized Federated Learning With Contrastive Shareable Representations for Label Heterogeneity in Non-IID Data
提出FedCoSR算法，通过共享浅层参数和典型局部表示，结合对比学习与自适应聚合，解决标签分布不均和数据稀缺导致的精度与公平性问题，适用于分布式智能通信场景。
Heterogeneity arising from label distribution skew and data scarcity can cause inaccuracy and unfairness in intelligent communication applications that heavily rely on distributed computing. To deal with it, this article proposes a novel personalized federated learning algorithm, named federated contrastive shareable representations (FedCoSRs), to facilitate knowledge sharing among clients while maintaining data privacy. Specifically, the parameters of local models' shallow layers and typical local representations are both considered as shareable information for the server and are aggregated globally. To address performance degradation caused by label distribution skew among clients, contrastive learning is adopted between local and global representations to enrich local knowledge. Additionally, to ensure fairness for clients with scarce data, FedCoSR introduces adaptive local aggregation to coordinate the global model involvement in each client. Our simulations demonstrate FedCoSR's effectiveness in mitigating label heterogeneity by achieving accuracy and fairness improvements over existing methods on datasets with varying degrees of label heterogeneity.