arXiv:2605.23988v1 Announce Type: cross Abstract: Adapting large AI models (LAMs) to personalized edge data is challenging because wireless devices have limited memory, computation, and uplink capacity. Federated fine-tuning preserves data privacy but still requires each device to host the full model, while split learning reduces device memory at the cost of heavy activation transmission. This paper proposes TSFLora, a token-compressed split fine-tuning framework for communication-efficient LAM adaptation at the edge. TSFLora combines attention-guided token selection, token merging, low-bit ac
The rapid advancement of large AI models (LAMs) and the increasing proliferation of edge devices necessitate efficient methods for deploying and fine-tuning AI at the periphery.
This development addresses critical limitations in memory, computation, and uplink capacity for deploying advanced AI on ubiquitous wireless edge devices, unlocking new applications and efficiencies.
Local fine-tuning of large AI models on resource-constrained devices becomes more feasible and communication-efficient, shifting the paradigm from purely cloud-centric AI to a more distributed model.
- · Edge Device Manufacturers
- · Telecommunications Companies
- · AI-as-a-Service Providers
- · Consumers of Edge AI Products
- · Cloud-only AI solutions
- · Developers reliant on high-bandwidth edge connections
More powerful and personalized AI applications become available directly on mobile phones, IoT devices, and other edge hardware.
The demand for specialized edge AI hardware and low-power AI accelerators will increase significantly, driving innovation in that sector.
Enhanced on-device AI capabilities could lead to new business models and services that prioritize data privacy and real-time processing without constant cloud reliance.
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Read at arXiv cs.LG