A model-agnostic framework combining GPBACC with robust aggregation and group testing for privacy-preserving and verifiable distributed learning in federated and decentralized settings.
Robust aggregation for federated learning,
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Privacy-Preserving and Verifiable Approximate Distributed Coded Computing
A model-agnostic framework combining GPBACC with robust aggregation and group testing for privacy-preserving and verifiable distributed learning in federated and decentralized settings.