Stream Learning: Partition-Fair Gossip Learning Without Tokens

arXiv:2608.06946 · cs.DC, cs.LG · Submitted 2026-08-07 · Read on arXiv

Fabien Mathieu, Alexandre Pham, Maria Gradinariu Potop-Butucaru, S{é}bastien Tixeuil

NPA · NPA · NPA · IUF, NPA

cs.DC, cs.LG

Submitted: 2026-08-07

Updated: 2026-08-10

Journal ref: SSS 2026, 28th International Symposium on Stabilization, Safety, and Security of Distributed Systems, Oct 2026, Gothenburg, Sweden

Code: https://github.com/makgyver/gossipy

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 46× in this paper · explained in 1 episode
• Federated Learning — 8× in this paper · explained in 19 episodes
• Decentralized Learning — 5× in this paper · explained in 2 episodes
• Data Heterogeneity — 3× in this paper · explained in 3 episodes
• Partitioned Token Gossip Learning — 3× in this paper
• Federated Learning (FL) — 2× in this paper · explained in 12 episodes
• Clarity — 1× in this paper · explained in 3 episodes
• Class Imbalance — 1× in this paper · explained in 3 episodes
• Decentralized Federated Learning — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Learning Paradigms — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Machine Learning (ML) — 1× in this paper · explained in 2 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• Mosaic Learning: A Framework for Decentralized Learning with Model Fragmentation
• Decentralized Federated Learning: A Segmented Gossip Approach

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