3 papers
cs.LG2026
Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs
Amr Abourayya, Jens Kleesiek, Michael Kamp
Federated fine-tuning of large language models is commonly formulated as a parameter aggregation problem. However, even parameter-efficient methods require transmitting large colle…
cs.LG2025
Whom to Trust? Adaptive Collaboration in Personalized Federated Learning
Amr Abourayya, Jens Kleesiek, Bharat Rao +1
Data heterogeneity poses a fundamental challenge in federated learning (FL), especially when clients differ not only in distribution but also in the reliability of their prediction…
cs.LG2024
Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning
Amr Abourayya, Jens Kleesiek, Kanishka Rao +4
In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been p…