English(EN)PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
新框架增强联邦学习的隐私性、鲁棒性和效率 · 跟踪4个来源
作者PulseAugur 编辑部·[7 个来源]·
研究人员正在开发先进的联邦学习（FL）框架，以增强隐私性、鲁棒性和效率。PRoVeFL利用跨多个服务器的多密钥全同态加密来防御推理和投毒攻击，显著提高了运行时间，优于先前的方法。另一种方法引入了一个自适应框架，通过使用局部降维和动态梯度裁剪来稳定训练并提高差分隐私下的模型性能，从而解决设备异构性和非独立同分布数据的问题。第三个系统FeLiX通过采用流感知可用性层和鲁棒聚合机制，专注于在真实场景中最小化达到准确率的挂钟时间，以应对客户端流失。最后，一个理论框架为交互式差分私有FL建立了van Trees不等式，定义了参数估计的minimax速率，并表明交互性并不能提高相对于更简单协议的速率。
AI
arXiv:2607.08651v1 Announce Type: new Abstract: Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or l…
Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants. Ledger-assisted federated lear…
arXiv:2607.06979v1 Announce Type: new Abstract: Federated Learning (FL) enables training shared models on private, on-device data, but production deployments remain constrained to slow, multi-day refresh cycles due to the complexity of coordinating massive client populations. For…
arXiv cs.AI
TIER_1English(EN)·Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca, Graham Cormode, Carsten Maple·
arXiv:2607.06612v1 Announce Type: cross Abstract: Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation …
arXiv cs.LG
TIER_1English(EN)·T. Tony Cai, Yicheng Li·
arXiv:2605.19813v2 Announce Type: replace Abstract: Federated differentially private protocols can communicate over many adaptive rounds and reuse each client's local samples. Existing lower bound arguments for federated DP are often restricted to noninteractive protocols or fres…
arXiv:2602.06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy. However, in practical deployments, device heterogeneity and non-independent and identically distributed (Non-IID) …
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditional FL frameworks rely on a centralized aggregation server and assume honest-but-curious clients, maki…
