AgentScope: A Flexible yet Robust Multi-Agent Platform

1 code implementation • 21 Feb 2024 • Dawei Gao, Zitao Li, Weirui Kuang, Xuchen Pan, Daoyuan Chen, Zhijian Ma, Bingchen Qian, Liuyi Yao, Lin Zhu, Chen Cheng, Hongzhu Shi, Yaliang Li, Bolin Ding, Jingren Zhou

With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications.

FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning

1 code implementation • 1 Sep 2023 • Weirui Kuang, Bingchen Qian, Zitao Li, Daoyuan Chen, Dawei Gao, Xuchen Pan, Yuexiang Xie, Yaliang Li, Bolin Ding, Jingren Zhou

When several entities have similar interested tasks, but their data cannot be shared because of privacy concerns regulations, federated learning (FL) is a mainstream solution to leverage the data of different entities.

Benchmarking Federated Learning +1

FedHPO-B: A Benchmark Suite for Federated Hyperparameter Optimization

1 code implementation • 8 Jun 2022 • Zhen Wang, Weirui Kuang, Ce Zhang, Bolin Ding, Yaliang Li

Due to this uniqueness, existing HPO benchmarks no longer satisfy the need to compare HPO methods in the FL setting.

Benchmarking Federated Learning +1

pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning

1 code implementation • 8 Jun 2022 • Daoyuan Chen, Dawei Gao, Weirui Kuang, Yaliang Li, Bolin Ding

Personalized Federated Learning (pFL), which utilizes and deploys distinct local models, has gained increasing attention in recent years due to its success in handling the statistical heterogeneity of FL clients.

Fairness Personalized Federated Learning

A Benchmark for Federated Hetero-Task Learning

1 code implementation • 7 Jun 2022 • Liuyi Yao, Dawei Gao, Zhen Wang, Yuexiang Xie, Weirui Kuang, Daoyuan Chen, Haohui Wang, Chenhe Dong, Bolin Ding, Yaliang Li

To investigate the heterogeneity in federated learning in real-world scenarios, we generalize the classic federated learning to federated hetero-task learning, which emphasizes the inconsistency across the participants in federated learning in terms of both data distribution and learning tasks.

Federated Learning Meta-Learning +2

FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning

1 code implementation • 12 Apr 2022 • Zhen Wang, Weirui Kuang, Yuexiang Xie, Liuyi Yao, Yaliang Li, Bolin Ding, Jingren Zhou

The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications.

Federated Learning Graph Learning

FederatedScope: A Flexible Federated Learning Platform for Heterogeneity

1 code implementation • 11 Apr 2022 • Yuexiang Xie, Zhen Wang, Dawei Gao, Daoyuan Chen, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, Jingren Zhou

Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challenges brought by various types of heterogeneity, including the heterogeneity in participants' local data, resources, behaviors and learning goals.

Federated Learning Hyperparameter Optimization

Coarformer: Transformer for large graph via graph coarsening

no code implementations • 29 Sep 2021 • Weirui Kuang, Zhen Wang, Yaliang Li, Zhewei Wei, Bolin Ding

We get rid of these obstacles by exploiting the complementary natures of GNN and Transformer, and trade the fine-grained long-range information for the efficiency of Transformer.