Ohio University

Decentralized federated learning has no central server, and link noise accumulates as models are relayed hop by hop, becoming more pronounced as the learning rate decays; Byzantine nodes can also mimic honest model averages to win loss-based selection. FedSharp keeps each predecessor's model copies at every node, screens out single-parameter deviations using coordinate-wise median deviations, averages the lowest-loss candidates, and adds noise-adaptive gradient-norm regularization with proximal training. On a 100-node ring with 33 Byzantine nodes, MNIST accuracy is 93.5%–94.9%, versus 29.4%–38.1% for a single-model-selection baseline.

Decentralized federated learning has no central server, and link noise accumulates as models are relayed hop by hop, becoming more pronounced as the learning rate decays; Byzantine nodes can also mimic honest model averages to win loss-based selection. FedSharp keeps each predecessor's model copies at every node, screens out single-parameter deviations using coordinate-wise median deviations, averages the lowest-loss candidates, and adds noise-adaptive gradient-norm regularization with proximal training. On a 100-node ring with 33 Byzantine nodes, MNIST accuracy is 93.5%–94.9%, versus 29.4%–38.1% for a single-model-selection baseline.