xqlin98 / Fair-yet-Equal-CMLLinks
This is the official implementation of the ICML 2023 paper "Fair yet Asymptotically Equal Collaborative Learning"
☆10Updated 2 years ago
Alternatives and similar repositories for Fair-yet-Equal-CML
Users that are interested in Fair-yet-Equal-CML are comparing it to the libraries listed below
Sorting:
- [ICML 2023] FedBR: Improving Federated Learning on Heterogeneous Data via Local Learning Bias Reduction☆27Updated last year
- [ICLR2023] Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning (https://arxiv.org/abs/2210.0022…☆40Updated 2 years ago
- A PyTorch implementation for the paper FedCon: A Contrastive Framework for Federated Semi-Supervised Learning.☆21Updated 3 years ago
- ICML2022: Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning☆41Updated 2 years ago
- (CVPR 2024) Communication-Efficient Federated Learning with Accelerated Client Gradient☆36Updated last year
- [NeurIPS 2023]Federated Learning with Bilateral Curation for Partially Class-Disjoint Data☆13Updated last week
- Federated Bilevel Optimization☆16Updated 3 years ago
- [ICML 2023] Optimizing the Collaboration Structure in Cross-Silo Federated Learning. Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He.☆18Updated 2 years ago
- [ICLR 2023] Test-time Robust Personalization for Federated Learning☆53Updated last year
- ☆17Updated last year
- Rethinking Data Heterogeneity in Federated Learning: Introducing a New Notion and Standard Benchmarks☆25Updated 2 years ago
- Diverse Client Selection for Federated Learning via Submodular Maximization☆32Updated 3 years ago
- Code release for Tackling Data Heterogeneity in Federated Learning with Class Prototypes appeared on AAAI2023.☆44Updated 2 years ago
- The is the official implementation of ICML 2023 paper "Revisiting Weighted Aggregation in Federated Learning with Neural Networks".☆57Updated last year
- Federated Learning - PyTorch☆14Updated 4 years ago
- ☆7Updated last year
- Personalized Federated Learning under Mixture of Distributions☆19Updated last year
- ☆18Updated 3 years ago
- ☆28Updated last year
- [ICLR 2023] "Combating Exacerbated Heterogeneity for Robust Models in Federated Learning"☆31Updated 2 years ago
- Code for novel methods for one-shot Federated Learning under high statistical heterogeneity.☆18Updated last year
- The pytorch code of FedDISC (Federated Diffusion-Inspired Semi-supervised Co-training method)☆7Updated last year
- ☆33Updated 2 years ago
- Practical One-Shot Federated Learning for Cross-Silo Setting☆42Updated 4 years ago
- [NeurIPS 2022] SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training☆41Updated 2 years ago
- Official code for "Personalized Federated Learning through Local Memorization" (ICML'22)☆42Updated 2 years ago
- ☆19Updated last year
- Code Implementation and Informations about FedAS☆26Updated 6 months ago
- The implementation of "Continual Local Training for Better Initialization of Federated Models" (ICIP 2020).☆17Updated 5 years ago
- ☆13Updated 3 years ago