Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
Federated Composite Optimization (FCO) has emerged as a promising framework for training models with structural constraints (e.g., sparsity) in distributed edge networks. However, simultaneously achieving communication efficiency and convergence robustness remains a significant challenge, particularly when dealing with non-smooth regularizers, statistical heterogeneity, and the restrictions of biased compression. To address these issues, we propose FedCEF (Federated Composite Error Feedback), a novel algorithm tailored for non-convex FCO. FedCEF introduces a decoupled proximal update scheme that separates the proximal operator from communication, enabling clients to handle non-smooth term...
| Authors | Pu Qiu, Chen Ouyang, Yongyang Xiong et al. |
|---|---|
| Primary category | math.OC |
| Published | 8 March 2026 |
| Last updated at source | 8 March 2026 |
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