Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage
arXiv:2605.09986v2 Announce Type: replace-cross Abstract: Training a language model on data scattered across bandwidth-limited nodes that cannot be centralized is a setting that arises in clinical networks, enterprise knowledge bases, and scientific consortia. We study the regime in which data must remain distributed across nodes, and ask what statistical guarantees are in principle achievable under explicit bandwidth budgets; we aim to characterize what is provably possible, not to demonstrate a deployment-ready system. Existing theory treats either training-time consistency or inference-time
The increasing scale and sensitivity of language models necessitate new approaches to training on distributed, bandwidth-constrained data, making federated learning with strong statistical guarantees particularly relevant now.
This research provides theoretical underpinnings for federated language model training under bandwidth constraints, which is crucial for applications where data cannot be centralized due to regulatory, privacy, or infrastructure limitations.
The explicit characterization of achievable statistical guarantees under bandwidth budgets allows for more informed design and deployment of privacy-preserving and efficient distributed AI systems.
- · Healthcare sector
- · Enterprise AI solutions
- · Federated learning researchers
- · Data privacy technologies
- · Centralized cloud AI providers (in specific use cases)
- · Organizations with rigid data governance policies
More robust and privacy-preserving AI development will become feasible for sensitive datasets across various industries.
This could accelerate the adoption of distributed AI architectures, reducing reliance on massive data transfers to centralized clouds.
It might foster new regulatory frameworks for localized AI processing and data residency, impacting global data flows and cloud market dominance.
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Read at arXiv cs.LG