Authors - Yazhiniyan Tamizhnambi, Senthil Prakash P.N Abstract - Having trustworthy systems in a decentralized systems remains a challenge, especially in adversarial conditions that include model poisoning, sigil attacks and unauthorized re-entries. Despite the fact that federated learning and swarm learning can achieve collaborative model training without sharing raw data, existing methodologies largely use fixed identities, self-reported accuracy, or direct weight comparison, which in an open or semi-trust environment is likely to be weak. This work presents a blockchain-based trust system in swarm learning, based on behavioral fingerprinting instead of identity-based accountability. In the suggested system, all involved nodes produce a behavioral fingerprint at every training round, which contains an accuracy of the challenge-sets, deviation of updating the model, and the distribution of features importance. The fingerprints are then stored on chain with the help of Merkle root structures, ensuring transparent behavioral tracking across rounds. To address early-time poisoning and delayed attacks, the system will utilize trust-weighted round-gated aggregation where the model updates will be verified before affecting other participants. Trust is measured through short-term and long-term consistency of behavior supported by Round Performance Score (RPS) which measures inconsistency with peer consensus during a round. The framework further resists Sybil and reentry attacks by matching behavioral fingerprints across identities, ensuring that malicious models cannot bypass detection by resetting node credentials. Behavioral fingerprints are matched across identities to stop further Sybil and re-entry attacks. This ensures credential resetting by nodes to bypass detection, since the behavior of the model will more or less be the same. The experimental analysis of heterogeneous hospital data sets shows improved universal accuracy, adequate poisoned updates mitigation, and dependable detection of malicious re-entry strategies.
