MLSP-P7: Federated and Distributed Machine Learning Systems III
Poster
Tue, 5 May, 14:00 - 16:00
Location: Poster Area 10
Session Type: Poster
Track: Machine Learning for Signal Processing [ML]
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MLSP-P7.1: RBA: TOWARDS ROBUST AND STEALTHY BACKDOOR ATTACK IN FEDERATED LEARNING
Junjie Xiang, Yuan Liu, Rui Zhang, Jiangnan University, China

MLSP-P7.2: Revisiting Backdoor Threat in Federated Instruction Tuning from a Signal Aggregation Perspective
Haodong Zhao, Jinming Hu, Gongshen Liu, Shanghai Jiao Tong University, China

MLSP-P7.3: CEFL-Ranking:Re-evaluation of Communication-Efficient FL Methods
Jiarong Li, Yuhao Zhou, Qing Ye, Jiancheng Lv, SiChuan University, China

MLSP-P7.4: PLUG-AND-PLAY ROBUST VISION ENCODERS FOR MULTI-MODAL LARGE LANGUAGE MODELS VIA FULLY MULTI-MODAL ADVERSARIAL FINETUNING
Mengjun Xu, Ruibo Guo, Tengyuan Liu, 中国科学技术大学, China; Ziqiang Li, 南京信息工程大学, China; Lei Liu, Bin Li, 中国科学技术大学, China

MLSP-P7.5: FEDERATED JOINT LEARNING FOR DOMAIN AND CLASS GENERALIZATION
Haoran Xu, Zhejiang University, China; Jiaze Li, Jianzhong Ju, Zhenbo Luo, Xiaomi Inc., China

MLSP-P7.6: RoPFL: Robust and Privacy-Preserving Decentralized Federated Learning Framework
Ke Li, Xu Yang, Zixiang Ban, Saiyu Qi, Zheng He, Hongguang Zhao, Qiuhao Wang, Xi'an Jiaotong University, China

MLSP-P7.7: Sparsity-Induced Reparametrization for Differentially Private Federated Learning
Jinshan Lai, Fengchun Zhang, University of Electronic Science and Technology of China, China; Dongfen Li, Chengdu University of Technology, China; Fengli Zhang, Ruijin Wang, University of Electronic Science and Technology of China, China; Yang Zhang, The Second Research Institute of CAAC, China

MLSP-P7.8: SPGR: SOURCE-PATH GUIDED REPAIR FOR DEEP NEURAL NETWORKS
Liming Bao, Yan Wang, Tao Sun, Inner Mongolia University, China

MLSP-P7.9: Conflict-Aware Client Selection for Multi-Server Federated Learning
Mingwei Hong, Xiamen University of Technology, China; Zheng Lin, The University of Hong Kong, China; Zehang Lin, Lin Li, Miao Yang, Xia Du, Xiamen University of Technology, China; Zihan Fang, City University of Hong Kong, China; Zhaolu Kang, Peking University, China; Dianxin Luan, University of Edinburgh, United Kingdom of Great Britain and Northern Ireland; Shunzhi Zhu, Xiamen University of Technology, China

MLSP-P7.10: CODED ROBUST AGGREGATION FOR DISTRIBUTED LEARNING UNDER BYZANTINE ATTACKS
Chengxi Li, Ming Xiao, Mikael Skoglund, KTH Royal Institute of Technology, Sweden