FED-FSTQ: Fisher-Guided Token Quantization for Communication-Efficient Federated Fine-Tuning of LLMs on Edge Devices
arXiv:2604.25421v2 Announce Type: replace Abstract: Federated fine-tuning provides a practical route to adapt large language models (LLMs) on edge devices without centralizing private data, yet in mobile deployments the training wall-clock is often bottlenecked by straggler-limited uplink communication under heterogeneous bandwidth and intermittent participation. Although parameter-efficient fine-tuning (PEFT) reduces trainable parameters, per-round payloads remain prohibitive in non-IID regimes, where uniform compression can discard rare but task-critical signals. We propose Fed-FSTQ, a Fishe
The proliferation of LLMs and edge devices necessitates communication-efficient fine-tuning methods to overcome network bottlenecks in real-world mobile deployments.
This research addresses a critical technical bottleneck for deploying powerful AI models directly on user devices, improving privacy and responsiveness while reducing reliance on centralized compute.
The ability to fine-tune LLMs effectively and privately on heterogeneous edge devices is significantly enhanced, making federated learning more practical for widespread AI adoption.
- · Edge device manufacturers
- · Federated learning researchers
- · Mobile AI application developers
- · Users concerned with data privacy
- · Centralized cloud AI providers (potentially less data)
- · Communication infrastructure providers (less traffic for fine-tuning)
More sophisticated and personalized AI models can be deployed on smartphones, wearables, and other edge devices.
Increased adoption of federated learning could lead to new privacy-preserving AI services and business models.
The reduced need for off-device communication for training paves the way for a more distributed and robust AI ecosystem, less vulnerable to network outages or censorship.
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Read at arXiv cs.LG