Secure aggregation scheme supports heterogeneous client quantization in federated learning
It avoids leaking partial aggregates when clients quantize differently and up to T semi-honest clients collude with the server.
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It avoids leaking partial aggregates when clients quantize differently and up to T semi-honest clients collude with the server.
The method uses DP rank selection, a DP power method, and a noisy low-rank covariance release under secure aggregation.
One adaptive scheme meets the optimal masked-gradient communication rate for every feasible dropout set under T-colluding security.
Secure federated learning cuts information-theoretic key consumption about 35-fold for chest X-ray training.
Researchers show colluding nodes in decentralized federated learning can reconstruct private updates despite secure aggregation, using lattice-based attacks tied to the Hidden Subset Sum Problem.
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