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cs.LG2025
Fed MobiLLM: Efficient Federated LLM Fine-Tuning over Heterogeneous Mobile Devices via Server Assisted Side-Tuning
Xingke Yang, Liang Li, Sicong Li +6
Collaboratively fine-tuning (FT) large language models (LLMs) over heterogeneous mobile devices fosters immense potential applications of personalized intelligence. However, such a…
cs.LG2025
PAE MobiLLM: Privacy-Aware and Efficient LLM Fine-Tuning on the Mobile Device via Additive Side-Tuning
Xingke Yang, Liang Li, Zhiyi Wan +6
There is a huge gap between numerous intriguing applications fostered by on-device large language model (LLM) fine-tuning (FT) from fresh mobile data and the limited resources of a…
cs.LG2025
Adaptive Budget Allocation for Orthogonal-Subspace Adapter Tuning in LLMs Continual Learning
Zhiyi Wan, Wanrou Du, Liang Li +2
Large language models (LLMs) often suffer from catastrophic forgetting in continual learning (CL) scenarios, where performance on previously learned tasks degrades severely while t…