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FL-GUARD: A Holistic Framework for Run-Time Detection and Recovery of Negative Federated Learning
March 8, 2024, 5:41 a.m. | Hong Lin, Lidan Shou, Ke Chen, Gang Chen, Sai Wu
cs.LG updates on arXiv.org arxiv.org
Abstract: Federated learning (FL) is a promising approach for learning a model from data distributed on massive clients without exposing data privacy. It works effectively in the ideal federation where clients share homogeneous data distribution and learning behavior. However, FL may fail to function appropriately when the federation is not ideal, amid an unhealthy state called Negative Federated Learning (NFL), in which most clients gain no benefit from participating in FL. Many studies have tried to …
abstract arxiv behavior cs.ai cs.dc cs.lg data data privacy detection distributed distribution federated learning federation framework however massive negative privacy recovery type
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