Federated aggregation robustness evaluated across five methods, five datasets, five architectures, and four attack conditions including clean, sign-flipping, Gaussian, and BadNets (reconstructed 500-cell seed-1 evaluation).
Read the original at arxiv.org→arXiv:2608.11423v1 Announce Type: new Abstract: Robust comparisons of federated aggregation methods require joint consideration of predictive performance, threat definitions, metric semantics, and execution...
Original headline: "Analysis of Federated Aggregation under Model Poisoning and Backdoor Attacks: A Reconstructed Cross-Dataset and Cross-Architecture Benchmark"
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- Aug 13, 04:00 UTC arXiv cs.LG lead source Analysis of Federated Aggregation under Model Poisoning and Backdoor Attacks: A Reconstructed Cross-Dataset and Cross-Architecture Benchmark