116 citations · 118 across the 5 of their papers we have counts for
6 papers · 1 filter
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer
Xin Gao, Xin Yang, Hao Yu +2
Federated Class-Incremental Learning (FCIL) focuses on continually transferring the previous knowledge to learn new classes in dynamic Federated Learning (FL). However, existing me…
Open Continual Feature Selection via Granular-Ball Knowledge Transfer
Xuemei Cao, Xin Yang, Shuyin Xia +2
This paper presents a novel framework for continual feature selection (CFS) in data preprocessing, particularly in the context of an open and dynamic environment where unknown clas…
Multi-granularity Knowledge Transfer for Continual Reinforcement Learning
Chaofan Pan, Lingfei Ren, Yihui Feng +4
Continual reinforcement learning (CRL) empowers RL agents with the ability to learn a sequence of tasks, accumulating knowledge learned in the past and using the knowledge for prob…
Federated Continual Learning via Knowledge Fusion: A Survey
Xin Yang, Hao Yu, Xin Gao +3
Data privacy and silos are nontrivial and greatly challenging in many real-world applications. Federated learning is a decentralized approach to training models across multiple loc…
Learning to Prompt Knowledge Transfer for Open-World Continual Learning
Yujie Li, Xin Yang, Hao Wang +2
This paper studies the problem of continual learning in an open-world scenario, referred to as Open-world Continual Learning (OwCL). OwCL is increasingly rising while it is highly…
Urban flows prediction from spatial-temporal data using machine learning: A survey
Peng Xie, Tianrui Li, Jia Liu +3
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors,…