[Submitted on 30 Jun 2026]
Title:TallyTrain: Communication-Efficient Federated Distillation
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Abstract:Federated learning is bandwidth-bound on two orthogonal axes: model size, which limits how often parameter-averaging methods can afford to merge, and class count, which makes per-probe soft-label distillation prohibitive at large vocabularies. Both ceilings tighten as modern systems scale. We collapse the class-count axis to $\lceil \log_2 C \rceil$ bits per probe by transmitting only each peer's $\arg\max$ class index, where $C$ is the number of output classes. The resulting protocol, TallyTrain, is not merely compressed: under non-IID training it can be preferable to soft-label distillation, because under-trained peers are confidently wrong and majority voting filters this noise where soft-label averaging amplifies it. Across standard benchmarks, TallyTrain matches or beats soft-label distillation at up to three orders of magnitude less communication. We also relax the model-size axis: we compose the cheap hard-label consensus with sparse parameter merges to obtain a bandwidth-bridge variant, which Pareto-dominates every tested operating point of the standard FedAvg, FedProx and FedDF baselines.
Comments: 27 pages, 7 figures, 12 tables
Subjects:
Machine Learning (cs.LG)
Cite as: arXiv:2607.00173 [cs.LG]
(or arXiv:2607.00173v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.00173
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Radhakrishna Achanta [view email] [v1] Tue, 30 Jun 2026 20:47:33 UTC (164 KB)
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