most citedBlockchain-enabled Trustworthy Federated Unlearning

1 citations · 2 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SD20241 cited

Generative Semantic Communication for Text-to-Speech Synthesis

Jiahao Zheng, Jinke Ren, Peng Xu +5

Semantic communication is a promising technology to improve communication efficiency by transmitting only the semantic information of the source data. However, traditional semantic…

eess.SP2024

Movable-Antenna Array Empowered ISAC Systems for Low-Altitude Economy

Ziming Kuang, Wenchao Liu, Chunjie Wang +4

This paper investigates a movable-antenna (MA) array empowered integrated sensing and communications (ISAC) over low-altitude platform (LAP) system to support low-altitude economy…

eess.SP2024

Joint Signal Detection and Automatic Modulation Classification via Deep Learning

Huijun Xing, Xuhui Zhang, Shuo Chang +4

Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this…

cs.CV2024

Instance-free Text to Point Cloud Localization with Relative Position Awareness

Lichao Wang, Zhihao Yuan, Jinke Ren +2

Text-to-point-cloud cross-modal localization is an emerging vision-language task critical for future robot-human collaboration. It seeks to localize a position from a city-scale po…

cs.LG2024

Scalable Federated Unlearning via Isolated and Coded Sharding

Yijing Lin, Zhipeng Gao, Hongyang Du +4

Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the fede…

cs.LG20241 cited

Blockchain-enabled Trustworthy Federated Unlearning

Yijing Lin, Zhipeng Gao, Hongyang Du +3

Federated unlearning is a promising paradigm for protecting the data ownership of distributed clients. It allows central servers to remove historical data effects within the machin…