![[2511.16992] FIRM: Federated In-client Regularized Multi-objective Alignment for Large Language Models](/static/img/curated/desks/afbytes-tech.png)
[2511.16992] FIRM: Federated In-client Regularized Multi-objective Alignment for Large Language Models
Abstract page for arXiv paper 2511.16992: FIRM: Federated In-client Regularized Multi-objective Alignment for Large Language Models
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[2606.01856] Boosting Multimodal Federated Learning via Chained Modality Optimization
Abstract page for arXiv paper 2606.01856: Boosting Multimodal Federated Learning via Chained Modality Optimization
[2303.04345] Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
Abstract page for arXiv paper 2303.04345: Federated Learning via Variational Bayesian Inference: Personalization, Sparsity and Clustering
[2606.01720] A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling
Abstract page for arXiv paper 2606.01720: A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling
[2605.30892] Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
Abstract page for arXiv paper 2605.30892: Bandwidth Allocation with Device Partitioning for Federated Learning over Industrial IoT networks
[2605.29002] FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
Abstract page for arXiv paper 2605.29002: FedQHD: Closed-Form Function-Space Federated Reinforcement Learning
[2605.27416] Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
Abstract page for arXiv paper 2605.27416: Can Quantum Federated Learning Withstand Circuit-Level Backdoors?
[2605.27900] Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
Abstract page for arXiv paper 2605.27900: Decoupled Training with Local Reinforcement Fine-Tuning in Federated Learning
[2605.28335] Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee
Abstract page for arXiv paper 2605.28335: Dimensionality Reduction for Robust Federated Learning: A Theoretical Analysis and Convergence Guarantee