3 papers
cs.LG2026
Trajectory-Aware Information Matching for Multi-Step Gradient Inversion in Federated Learning
Li Xia, Jing Yu, Zheng Liu +3
Federated learning enables distributed information sharing and collaborative model training without exposing raw client data. However, shared gradients or model updates may still c…
cs.LG2026
Communication-Efficient Neural Tangent Kernels for Heterogeneous Decentralized Federated Learning
Li Xia
Decentralized federated learning (DFL) enables collaborative model training without a central server, but converges slowly under statistical heterogeneity. Recent work has shown th…
cs.CR2026
VeriLLM: A Lightweight Framework for Publicly Verifiable Decentralized Inference
Ke Wang, Zishuo Zhao, Xinyuan Song +7
Decentralized inference provides a scalable and resilient paradigm for serving large language models (LLMs), enabling fragmented global resource utilization and reducing reliance o…