$FED federated

Latest papers

1. FedRIC: Heterogeneity-Aware Federated Learning with Hard Deadline Constraints in Open RAN eess.SY · 14 Oct · 2 hits

   …to the RIC due to its computational burden, timing constraints, and communication overhead, motivating Federated Learning (FL) as a distributed alternative. We formulate FL in O-RAN as a Mixed-Integer Nonlinear…

2. FUSE: Fairness-aware Update Screening against Byzantine Exploitation in $q$-Fair Federated Learning cs.LG · 14 Oct · 6 hits

   Client-level fair federated learning, in the form of $q$-fair federated learning ($q$-FFL), agnostic federated learning, or…

3. A Large-Dimensional Analysis of Least-Squares Federated Learning via Generalized Total Variation Minimization cs.LG · 14 Oct · 2 hits

   Federated learning over decentralized networks inherently trades off the suppression of local finite-sample…

4. Safe-FCL: Continual Risk Awareness and Fine-Grained Safety Aggregation for Federated Continual Learning of LLMs cs.LG · 14 Oct · 5 hits

   Federated fine-tuning enables large language models (LLMs) to learn collaboratively from distributed client…

5. Adaptive and Proximal Federated Optimization for Transferable Network Intrusion Detection cs.CR · 14 Oct · 6 hits

   …threats. This property called transferability across attack classes, remains underexplored especially in federated learning (FL) settings where data heterogeneity and client drift pose additional challenges. Prior…

6. PFedMA: Who Should Join Next as Federated Multi-Agent Orchestration Evolves? cs.AI · 14 Oct · 3 hits

   Federated Multi-Agent Systems (FedMAS) offer a promising paradigm for collaborative reasoning among LLM-based…

7. Federated Unlearning of Quantum Data via Classical Shadows quant-ph · 13 Oct · 4 hits

   Quantum federated learning trains a quantum neural network (QNN) across nodes that hold quantum data, such as the…

8. Tracing Knowledge Lineage for Federated Unlearning under Mutual Distillation cs.NI · 13 Oct · 2 hits

   …edge server and nearby devices can train language models of different sizes together by mutual distillation. Federated unlearning (FU) and unlearning for large language models (LLMs) execute a removal request on one…