Data-Topology Compatibility Enables Personalization in Decentralized Learning
Decentralized learning must balance collaboration with personalization across heterogeneous agents. This paper studies when cooperation can support statistically coherent groups of personalized models. We show that data-topology compatibility enables robust orbit-wise personalization: when local objectives and communication topology have (approximately) compatible automorphisms, agents within an orbit share (approximately) equal limiting distributions, while different orbits remain distinct without explicit personalization. This creates a new intermediate regime between global consensus and independent local learning. Under exact data-topology compatibility, as a reference system, we deco...
| Authors | Veronica Xu, Lingfei Wang, Chenlei Leng et al. |
|---|---|
| Primary category | stat.ML |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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