SONAR: Benchmarking Topology and Collaboration in Decentralized Learning#

Joyce Yuan, Yichuan Shi, Abhishek Singh, et al. (affiliations not listed in the program)

• Why: It treats topology as a first-class, controllable variable — and finds sparse structured graphs (rings, tori) can match or beat dense ones at far lower communication cost. That finding rhymes directly with datacenter fabric intuition, even though the domain is peer-to-peer federated learning, not DC networks.
• What: A modular framework unifying communication, topology management, and fine-grained telemetry for topology-aware decentralized learning, enabling reproducible measurement of performance, communication, robustness, and privacy. Key findings: topology's impact amplifies with scale and data heterogeneity; sparse structured topologies sit on a better efficiency frontier than dense graphs; and it identifies "collaborator collapse," a systematic failure mode where similarity-based neighbor selection reduces diversity and degrades generalization (from abstract only).
• How: Controlled, reproducible evaluation across topologies under a common measurement harness spanning performance, communication, robustness, and privacy dimensions.
• Where applicable: Decentralized/P2P learning research settings, not datacenter fabrics — but the topology-efficiency results and the collaborator-collapse failure mode are conceptually portable to any system where participants choose communication partners adaptively.