Author

Seyyedali Hosseinalipour

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Preprint Sep 2026

TRACER: Adaptive Multi-Robot Social Navigation via Joint Human-Response Prediction and Interaction-Aware Replanning

Multi-robot navigation in human-shared spaces is inherently interactive: coordinated robot motions influence how nearby entities respond, while those responses provide valuable information for subsequent robot decisions. However, existing methods typically address action-conditioned prediction, multi-robot planning, or...

Lan Hu, Minghui Liwang, Wen-Bo Zhu et al. · 0 citations

#machine learning Preprint Oct 2026

BLEND: Balancing Personalization vs. Generalization in Federated Vision-Language Models

Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within the fine-tuning pipelines of vision-language models (VLMs). In this work, we introduce BLEND, a new framework designed to jointly enhance the personalization and generali...

Payam Abdisarabshali, Fardis Nadimi, Kasra Borazjani et al. · 0 citations

#machine learning May 2026

Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning with Real-Time Feedback

This work introduces SPEAR (Self-Play Enhancement via Advantage-Weighted Refinement), an efficient online learning algorithm for federated LLM fine-tuning that requires only an interaction-level binary satisfaction signal together with non-answer feedback, without requiring a ground-truth answer to be provided, and doe...

Seohyun Lee, Wen-Zhi Fang, Dong-Jun Han et al. · arXiv.org · 1 citation

#machine learning Preprint Sep 2026

Fisher-Informed Recalibration for Feedback-Based On-Policy Self-Distillation of LLMs

Fire (Fisher-Informed REcalibration), a dual-branch framework that recalibrates the supervision applied to correct and incorrect on-policy outputs during fine-tuning, is proposed, which provides substantially more stable self-distillation while maintaining strong downstream performance, particularly in settings where s...

Seohyun Lee, Dong-Jun Han, Seyyedali Hosseinalipour et al. · 0 citations

Jul 2026

HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning

Her HermesHFL, a hierarchical federated learning framework that supports selective unlearning, dynamic client participation, and client reintegration for scalable LLM fine-tuning via parameter-efficient fine-tuning (PEFT) with LoRA, is proposed and developed.

Chenxi Sun, Minghui Liwang, Wu-Si He et al. · arXiv.org · 0 citations

#machine learning Jun 2026

Federated Foundation Models over Vehicular Networks

The promise of M3T FedFMs for vehicular networks is demonstrated and the implementation of the fundamental training/fine-tuning principles of M3T FedFMs are introduced to facilitate reproducibility and stimulate research in this emerging area.

Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali et al. · arXiv.org · 0 citations

#artificial intelligence Preprint Sep 2026

Bridging the Semantic-Utility Gap in Multimodal RAG via Generator-in-the-Loop Alignment

This work proposes a two-stage generator-in-the-loop alignment framework that consistently outperforms rank-order, random, and REPLUG-style likelihood baselines under various alignment losses and pool size settings, suggesting that answer-level generator feedback is an effective supervision signal for preference alignm...

Zhang-Yu Chang, Dong-Jun Han, Seyyedali Hosseinalipour et al. · 0 citations