A new paper proposes Personalized Federated Direct Preference Optimization (pFedDPO), letting each client keep a small local LoRA adapter on a shared base model to better fit heterogeneous…

#FederatedLearning #LoRA #PreferenceOptimization #PrivacyPreservingML
https://arxiv.org/abs/2610.12885

Fed-GRPO proposes a federated extension of Group Relative Policy Optimization for LLM fine-tuning, using GRPO's own reward statistics as zero-cost signals to drive aggregation, local training, and communication.…

#FederatedLearning #LLM #RLHF #PrivacyPreservingML
https://arxiv.org/abs/2610.11502

New arXiv work introduces Cocoon, a system architecture that improves differentially private training by managing correlated noises, addressing accuracy loss and overhead in large models…

#DifferentialPrivacy #MachineLearning #AIInfrastructure #PrivacyPreservingML
https://arxiv.org/abs/2510.07304