Achieving consistency in FedSAM using local adaptive distillation on sports image classification.

ABSTRACT: Federated learning (FL) is an effective distributed learning paradigm for protecting client privacy, enabling multiple clients to collaboratively train a global model without uploading private data. It has promising applications in sports image classification. However, FL faces the issue of non-independent and identically distributed (non-IID) data, which leads to excessive variance between local models and hinders the convergence of the global model. Although FedSAM and its variants attempt to reduce this variance by finding smooth solutions between local models, local smoothing does not necessarily result in global smoothing. We refer to this issue as the smoothness inconsistency problem. To address this challenge, we propose a novel FL paradigm, named A-FedSAM, which utilizes adaptive l

SUBMITTER: Zhen K 

PROVIDER: S-EPMC12533856 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature