Coward detects backdoors in federated learning by injecting a collision-suppressed watermark on OOD data to invert the detection paradigm and limit OOD bias effects.
Advances and open problems in federated learning
4 Pith papers cite this work. Polarity classification is still indexing.
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FedCoE proposes a coordinated dual-level MoE framework for federated learning that improves global and personalized accuracy while enabling strong cold-start performance for new clients.
For hierarchical secure aggregation with groupwise keys of size G>1, the optimal rate region is fully characterized with user and relay rates at least 1 and minimum groupwise key rate max of two combinatorial terms.
Introduces M-DSL algorithm for distributed swarm learning that selects workers using a new non-i.i.d. degree metric to improve convergence and accuracy under data heterogeneity, with theoretical analysis and experiments on heterogeneous datasets.
citing papers explorer
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Coward: Collision-based OOD Watermarking for Practical Proactive Federated Backdoor Detection
Coward detects backdoors in federated learning by injecting a collision-suppressed watermark on OOD data to invert the detection paradigm and limit OOD bias effects.
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FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
FedCoE proposes a coordinated dual-level MoE framework for federated learning that improves global and personalized accuracy while enabling strong cold-start performance for new clients.
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On the Capacity of Hierarchical Secure Aggregation with Groupwise Keys
For hierarchical secure aggregation with groupwise keys of size G>1, the optimal rate region is fully characterized with user and relay rates at least 1 and minimum groupwise key rate max of two combinatorial terms.
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Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Data
Introduces M-DSL algorithm for distributed swarm learning that selects workers using a new non-i.i.d. degree metric to improve convergence and accuracy under data heterogeneity, with theoretical analysis and experiments on heterogeneous datasets.