Blockchain-Verified Federated Learning with Cryptographic Adversarial Robustness

While Hiwale et al. (2021) and Dubey & Choubey (2023) explore blockchain for data integrity, they overlook adversarial manipulation of model updates. This research integrates blockchain to log encrypted model updates (using HE from Mahmood et al., 2025) and zero-knowledge proofs to verify computations without exposing data. Unlike Agarampalli & Siddareddy's (2025) AFRA-ML—which optimizes resources but assumes honest participants—this system detects and isolates malicious participants via cryptographic proofs. For instance, in healthcare FL (per Madavarapu et al., 2024), a hospital's model update would be encrypted and logged on-chain, with consensus nodes validating its correctness. The novelty is merging blockchain's immutability with HE's confidentiality to create a tamper-evident, privacy-preserving FL pipeline.

References:

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