Why Federated Optimization Fails to Achieve Perfect Fitting? A Theoretical Perspective on Client-Side Optima
"数据异质性" 相关文章
DP-FedPGN: Finding Global Flat Minima for Differentially Private Federated Learning via Penalizing Gradient Norm
CEPerFed: Communication-Efficient Personalized Federated Learning for Multi-Pulse MRI Classification
Federated Unlearning in the Wild: Rethinking Fairness and Data Discrepancy
SlimPack: Fine-Grained Asymmetric Packing for Balanced and Efficient Variable-Length LLM Training
Beyond Aggregation: Guiding Clients in Heterogeneous Federated Learning
Curriculum Guided Personalized Subgraph Federated Learning
Widening the Network Mitigates the Impact of Data Heterogeneity on FedAvg
FedSWA: Improving Generalization in Federated Learning with Highly Heterogeneous Data via Momentum-Based Stochastic Controlled Weight Averaging
Beyond Right to be Forgotten: Managing Heterogeneity Side Effects Through Strategic Incentives
Globalization for Scalable Short-term Load Forecasting
FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning
FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models
破解联邦学习中的辛普森悖论，浙大提出反事实学习新框架FedCFA
长序列预测 & 时空预测万字长文：一文带你探索多元时间序列预测的研究进展！
