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Found 5 papers, 1 papers with code
Beyond Noise: Privacy-Preserving Decentralized Learning with Virtual Nodes
We theoretically prove the convergence of Shatter and provide a formal analysis demonstrating how Shatter reduces the efficacy of attacks compared to when exchanging full models between participating nodes.
Fairness Auditing with Multi-Agent Collaboration
Existing work in fairness auditing assumes that each audit is performed independently.
QuickDrop: Efficient Federated Unlearning by Integrated Dataset Distillation
We introduce QuickDrop, an efficient and original FU method that utilizes dataset distillation (DD) to accelerate unlearning and drastically reduces computational overhead compared to existing approaches.
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication
We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches.
Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments
We show that when the training classes are non-i. i. d., Bristle significantly outperforms the accuracy of the most Byzantine-resilient baselines by 2. 3x while reducing communication costs by 90%.
