Consensus-based Decentralized Distributed Swarm Learning with Heterogeneous Big Data

2026-09-10

AI Summary

本文提出一种基于共识的去中心化分布式群学习框架，结合共识优化与粒子群优化，解决非凸目标、数据异质性及复杂无线网络拓扑问题。

Abstract

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.

Problem

• non-convex objectives
• data heterogeneity
• wireless network topology

Innovation

• consensus-based decentralized distributed swarm learning
• particle swarm optimization (PSO)
• heterogeneous big data
• adaptive neighbor-mixing strategy
• non-convex objectives