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cs.AI2026
FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs
Kahou Tam, Wei Niu, Yu Bao +3
Transformer-based models have enabled unprecedented capabilities across language, vision, and multimodal tasks. On-device fine-tuning of transformer models offers a privacy-preserv…
cs.DC2026
Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge
Yebo Wu, Jingguang Li, Chunlin Tian +3
Federated fine-tuning enables privacy-preserving LLM adaptation but faces a critical bottleneck: the disparity between LLMs' high memory demands and edge devices' limited capacity.…
cs.DC2026
Floe: Federated Specialization for Real-Time LLM-SLM Inference
Chunlin Tian, Kahou Tam, Yebo Wu +4
Deploying large language models (LLMs) in real-time systems remains challenging due to their substantial computational demands and privacy concerns. We propose Floe, a hybrid feder…