11 citations · 17 across the 5 of their papers we have counts for
7 papers
SWE-bench-java: A GitHub Issue Resolving Benchmark for Java
Daoguang Zan, Zhirong Huang, Ailun Yu +17
GitHub issue resolving is a critical task in software engineering, recently gaining significant attention in both industry and academia. Within this task, SWE-bench has been releas…
Heterogeneous Federated Learning with Splited Language Model
Yifan Shi, Yuhui Zhang, Ziyue Huang +4
Federated Split Learning (FSL) is a promising distributed learning paradigm in practice, which gathers the strengths of both Federated Learning (FL) and Split Learning (SL) paradig…
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models
Zihao Lin, Yan Sun, Yifan Shi +4
With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern. Altho…
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Yifan Shi, Yingqi Liu, Yan Sun +4
Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems. Howe…
Efficient Federated Learning with Enhanced Privacy via Lottery Ticket Pruning in Edge Computing
Yifan Shi, Kang Wei, Li Shen +4
Federated learning (FL) is a collaborative learning paradigm for decentralized private data from mobile terminals (MTs). However, it suffers from issues in terms of communication,…
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
Yifan Shi, Kang Wei, Li Shen +4
To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard f…