Tag: Federated Learning
All the articles with the tag "Federated Learning".
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Clients Collaborate: Flexible Differentially Private Federated Learning with Guaranteed Improvement of Utility-Privacy Trade-off
本文提出 FedCEO 框架,通过服务器端张量低秩优化和客户端语义互补性,在差分隐私联邦学习中实现了效用-隐私权衡的显著改进,理论上提升了 O(√d) 的界限,并在实验中验证了其优越性能。
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UnifyFL: Enabling Decentralized Cross-Silo Federated Learning
UnifyFL proposes a decentralized cross-silo federated learning framework using Ethereum blockchain and IPFS to enable trust-based collaboration among organizations, achieving comparable accuracy to centralized FL with flexible aggregation policies and efficient handling of stragglers through synchronous and asynchronous modes.
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Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models
本文提出了一种无线联邦LoRA微调框架,通过Sparsified Orthogonal Fine-Tuning (SOFT) 和Two Stage Federated Algorithm (TSFA) 优化参数稀疏化和动态资源分配,提高了通信效率和学习性能。