This paper introduces FEDDE, a general and efficient framework that addresses data redundancy across clients to facilitate effective federated learning (FL). At its core, FEDDE adopts a hierarchical deduplication architecture where clients first …
Federated learning (FL) has emerged as a popular paradigm for distributed machine learning over decentralized data. Data generated by FL clients is prone to noises. While the impact of data noise on centralized learning (CL) is well understood, there …