Advances in Robust Federated Learning: Heterogeneity Considerations

no code implementations • 16 May 2024 • Chuan Chen, Tianchi Liao, Xiaojun Deng, Zihou Wu, Sheng Huang, Zibin Zheng

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and collaboratively train models across multiple clients with different data distributions, model structures, task objectives, computational capabilities, and communication resources.

Federated Learning Privacy Preserving