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This paper introduces a model-agnostic framework for secure distributed learning that simultaneously addresses privacy preservation and adversarial threats in both federated and decentralized settings. By integrating privacy-enhancing coded computing (GPBACC) with robust aggregation strategies and lightweight verification techniques, the authors demonstrate significant reductions in privacy leakage and improved resilience against active adversaries. The explicit attack-driven evaluation reveals that their approach is not only effective but also practical for real-world deployments of distributed machine learning.
Privacy-enhancing coded computing can effectively shield distributed learning from adversarial attacks while maintaining model performance across various architectures.
Distributed machine learning enables collaborative model training without centralizing data, but it also exposes learning processes to privacy leakage and malicious manipulation. Existing defenses typically address these threats in isolation and are often tailored to specific learning paradigms or model architectures, limiting their applicability in realistic deployments. In particular, federated learning and decentralized learning exhibit distinct adversarial surfaces that are rarely addressed within a unified framework. In this paper, we present a model-agnostic framework for adversary-resistant distributed learning that jointly addresses privacy preservation and malicious behavior across both federated and decentralized settings. Our approach combines paradigm-specific defense mechanisms with GPBACC, a privacy-enhancing coded computing technique applicable to arbitrary machine learning models. For federated learning, we integrate robust aggregation strategies to mitigate the impact of malicious participants, while for decentralized learning we employ approximate decode-and-compare and group testing techniques to enable lightweight verification and adversary isolation without relying on a trusted aggregator. Crucially, we evaluate the proposed framework through an explicit, attack-driven analysis. We implement representative privacy attacks and malicious behaviors, and empirically demonstrate that the combination of GPBACC with robust aggregation and verification mechanisms significantly reduces privacy leakage and improves resilience against active adversaries. These results suggest that privacy-enhancing coded computing, when combined with appropriate adversary-resistance strategies, provides a practical and deployable foundation for secure distributed machine learning.
