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Fisher-Informed Recalibration for Feedback-Based On-Policy Self-Distillation of LLMs
Seohyun Lee, Dong-Jun Han, Seyyedali Hosseinalipour +1
Feedback-based on-policy self-distillation has emerged as a promising approach for enabling foundation models, more specifically Large Language Models (LLMs), to learn from their o…
Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning
Seohyun Lee, Wenzhi Fang, Dong-Jun Han +2
Recent works have advanced feedback-based learning systems, whereby a foundation model is able to intake incoming feedback (e.g., from a user) to self-improve, creating a self-loop…
TAP: Two-Stage Adaptive Personalization of Multi-Task and Multi-Modal Foundation Models in Federated Learning
Seohyun Lee, Wenzhi Fang, Dong-Jun Han +2
In federated learning (FL), local personalization of models has received significant attention, yet personalized fine-tuning of foundation models remains underexplored. In particul…
Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning
Seohyun Lee, Wenzhi Fang, Anindya Bijoy Das +3
Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While the…
Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning
Seohyun Lee, Anindya Bijoy Das, Satyavrat Wagle +1
One of the main challenges of decentralized machine learning paradigms such as Federated Learning (FL) is the presence of local non-i.i.d. datasets. Device-to-device transfers (D2D…