LFHE: Local-First Heuristic Evolution for Bounded Local Topology Search in Decentralized Learning with Non-IID Data
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The paper investigates how scaling the number of clients in decentralized learning affects information propagation, consensus, and accuracy. By comparing a degree‑two Ring, a Static Random graph, and a locally adaptive Local‑First Heuristic Evolution (LFHE) topology on CIFAR‑10, the authors show that the Ring’s spectral gap decays as Θ(N⁻²), leading to slower convergence and higher disagreement. Experiments reveal that keeping local dataset size constant mitigates population penalties, and that LFHE can improve accuracy and consensus at the cost of increased communication.
The paper introduces connectivity-aware client selection for decentralized federated learning, using spectral graph tools to choose a subset of structurally relevant neighbors for each client. By leveraging eigenvector embeddings and heat/diffusion kernel similarities, the method reduces communication overhead while maintaining or improving accuracy compared to uniform subsampling and full broadcasting. Experiments show up to a 3‑percentage‑point accuracy gain under strong data heterogeneity and a 50% reduction in transmitted messages.
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