基于共识的异构大数据去中心化分布式群体学习
Consensus-based Decentralized Distributed Swarm Learning with Heterogeneous Big Data
- Georgia State University（佐治亚州立大学）
- University of Louisiana at Lafayette（路易斯安那大学拉斐特分校）
- George Mason University（乔治梅森大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对边缘智能中非凸目标、数据异构和复杂网络拓扑的挑战，提出融合共识优化与粒子群优化的去中心化分布式群体学习框架，实现无需原始数据交换的协作，并缓解异构数据导致的性能退化。
AI中文摘要：
人工智能日益依赖于边缘设备收集的大规模、分布式和异构数据。然而，由于非凸目标、数据异构性和复杂的无线网络拓扑，边缘智能的实践仍然具有挑战性。为解决这些问题，本文提出了一种面向无线边缘网络的基于共识的去中心化分布式群体学习（CD-DSL）框架。我们的CD-DSL将共识优化与粒子群优化（PSO）相结合，在利用PSO的探索与开发能力的同时，实现相邻设备间的模型共识。共识机制支持无需原始数据交换的去中心化协调，而受PSO启发的更新利用历史经验和邻居共享经验来增强非凸优化的探索能力，提高对数据异构性的鲁棒性，并加速收敛。我们进一步开发了一种自适应邻居混合策略，学习性能感知的共识权重，以改善异构边缘设备间的去中心化协作。理论分析表明，CD-DSL保持参与者一致性，并在非凸目标下实现到驻点邻域的非遍历收敛。实验结果表明，CD-DSL能够缓解现有去中心化基线因异构数据导致的性能退化。
英文摘要：
Artificial intelligence increasingly relies on large-scale, distributed, and heterogeneous data collected by edge devices. However, the practice of edge intelligence remains challenging due to non-convex objectives, data heterogeneity, and complex wireless network topology. To address these issues, this paper proposes a consensus-based decentralized distributed swarm learning (CD-DSL) framework for wireless edge networks. Our CD-DSL integrates consensus optimization with particle swarm optimization (PSO), by reaching the model consensus among neighboring devices while leveraging the PSO exploration and exploitation. The consensus mechanism supports decentralized coordination without raw-data exchange, while PSO-inspired updates utilize historical and neighbor-shared experience to enhance exploration for non-convex optimization, improve robustness to data heterogeneity, and accelerate convergence. We further develop an adaptive neighbor-mixing strategy that learns performance-aware consensus weights, improving decentralized collaboration among heterogeneous edge devices. Theoretical analysis establishes that CD-DSL maintains participant consistency and achieves non-ergodic convergence to a neighborhood of a stationary point under non-convex objectives. Experimental results show that CD-DSL can mitigate the performance degeneration of existing decentralized baselines caused by heterogeneous data.
