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Decentralized Nonconvex Robust Optimization Over Unsafe Multiagent Systems: System Modeling, Utility, Resilience, and
IEEE Transactions on Cybernetics
|June 13, 2025
Summary
This study introduces DP-SCC-PL, a novel algorithm for multiagent systems (MASs) facing privacy leakage and Byzantine threats. It enables secure optimization by masking gradients and using resilient aggregation, ensuring reliable learning.
Area of Science:
- Distributed Optimization
- Multiagent Systems (MASs)
- Machine Learning Security
Background:
- Privacy leakage and Byzantine failures disrupt multiagent system optimization.
- Existing methods struggle with nonconvex optimization problems under these adversarial conditions.
Purpose of the Study:
- To develop a decentralized stochastic gradient algorithm resilient to both privacy leakage and Byzantine attacks.
- To address nonconvex optimization problems in unsafe multiagent systems.
Main Methods:
- Developed an unsafe multiagent system model.
- Implemented gradient masking with Gaussian noise and a resilient aggregation method (self-centered clipping - SCC).
- Designed a differentially private (DP) and Byzantine-resilient (BR) algorithm: DP-SCC-PL.
Main Results:
- DP-SCC-PL achieves consensus among reliable agents despite DP and BR mechanisms.
- Convergence analysis reveals a trade-off between algorithm utility, resilience, and privacy.
- Asymptotic exact convergence is recoverable when privacy and Byzantine issues are absent.
Conclusions:
- DP-SCC-PL effectively tackles nonconvex optimization in unsafe multiagent systems.
- The algorithm demonstrates utility, resilience, and privacy in numerical experiments.
- Theoretical analysis confirms the algorithm's convergence properties and the inherent trilemma.
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