DFedReweighting is a unified reweighting method for decentralized federated learning that customizes aggregation via target metrics and strategies to improve fairness, Byzantine robustness, and other objectives while proving linear convergence under standard assumptions.
A lit- tle is enough: Circumventing defenses for distributed learn- ing.Advances in Neural Information Processing Systems, 32, 2019
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DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning
DFedReweighting is a unified reweighting method for decentralized federated learning that customizes aggregation via target metrics and strategies to improve fairness, Byzantine robustness, and other objectives while proving linear convergence under standard assumptions.