Population Scaling or Data Dilution? Dynamics of Local Topology Evolution in Decentralized Learning

Excerpt

arXiv:2610.05476v1 Announce Type: cross Abstract: Scaling decentralized learning changes not only the number of clients $N$, but also the dynamics of information propagation and consensus. We argue that the effect of increasing $N$ cannot be understood in isolation, because data allocation, topology-dependent mixing, and communication capacity may change simultaneously. We study these coupled effects on CIFAR-10 with $N\in\{10,50,100,200\}$, comparing a degree-two Ring, a Static Random graph, an