Fady Rezk — accepted AI-conference papers

2026

FedP²EFT: Federated Learning to Personalize PEFT for Multilingual LLMs

Royson Lee, Minyoung Kim, Fady Rezk, Rui Li, Stylianos I. Venieris, Timothy Hospedales

AAAI 2026technical

Federated learning (FL) has enabled training of multilingual large language models (LLMs) on diverse and decentralized multilingual data, especially on low-resource languages. To improve client-specific performance, personalization via the use of parameter-efficient fine-tuning (PEFT) modules such a

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2026

Weight-Space Learning for Certifiable Few-shot Transfer Learning

Fady Rezk, Royson Lee, Henry Gouk, Timothy Hospedales, Minyoung Kim

ICML 2026poster

In contemporary deep learning, a prevalent and effective workflow for solving low-data problems is adapting powerful pre-trained foundation models (FMs) to new tasks via parameter-efficient fine-tuning (PEFT). However, while empirically effective, the resulting solutions lack generalisation guarante…

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