Matching Rates and Optimal Allocation for Federated Probe-Logit Distillation under Heterogeneous Bandwidth Budgets
arXiv:2605.29642v1 Announce Type: cross Abstract: In federated language modeling, $K$ nodes each hold $n$ samples but cannot pool data or exchange full-precision gradients or weights. We study the minimax rate at which a conditional distribution over $V$ tokens can be estimated when each node may upload at most $B$ bits per query in a public probe set. In federated probe-logit distillation (FPLD), each node transmits a scalar-quantized logit vector on the probe set, and an aggregator distills a global parametric student. Prior work (Dubey and Huo, 2026) establishes a high-probability KL rate $
This paper addresses a critical technical challenge in federated learning for large language models, a field rapidly evolving due to privacy concerns and distributed data needs.
Improving efficiency in federated learning is crucial for scaling AI applications, especially in contexts where data privacy and bandwidth limitations are significant constraints.
This research provides theoretical advancements that could lead to more robust and resource-efficient federated AI models, enabling broader deployment across distributed environments.
- · AI developers
- · Cloud service providers
- · Privacy-focused industries
- · Edge computing
- · Centralized data monopolies
More efficient federated language models will become feasible for deployment in privacy-sensitive sectors.
Increased adoption of federated learning could democratize AI development by reducing the need for massive centralized datasets.
This could accelerate the development of personalized AI services that run closer to the data source, potentially shifting value away from large data aggregators.
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