237 citations · 723 across the 30 of their papers we have counts for
20 papers · 1 filter
Towards Personalized Federated Learning via Heterogeneous Model Reassembly
Jiaqi Wang, Xingyi Yang, Suhan Cui +4
This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures.…
MAS: Towards Resource-Efficient Federated Multiple-Task Learning
Weiming Zhuang, Yonggang Wen, Lingjuan Lyu +1
Federated learning (FL) is an emerging distributed machine learning method that empowers in-situ model training on decentralized edge devices. However, multiple simultaneous FL tas…
FedSampling: A Better Sampling Strategy for Federated Learning
Tao Qi, Fangzhao Wu, Lingjuan Lyu +2
Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for…
When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
Weiming Zhuang, Chen Chen, Jingtao Li +3
The intersection of Foundation Model (FM) and Federated Learning (FL) presents a unique opportunity to unlock new possibilities for real-world applications. On the one hand, FL, as…
FedWon: Triumphing Multi-domain Federated Learning Without Normalization
Weiming Zhuang, Lingjuan Lyu
Federated learning (FL) enhances data privacy with collaborative in-situ training on decentralized clients. Nevertheless, FL encounters challenges due to non-independent and identi…
Reducing Communication for Split Learning by Randomized Top-k Sparsification
Fei Zheng, Chaochao Chen, Lingjuan Lyu +1
Split learning is a simple solution for Vertical Federated Learning (VFL), which has drawn substantial attention in both research and application due to its simplicity and efficien…
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection
Rui Song, Lingjuan Lyu, Wei Jiang +2
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by tr…
Byzantine-Robust Learning on Heterogeneous Data via Gradient Splitting
Yuchen Liu, Chen Chen, Lingjuan Lyu +3
Federated learning has exhibited vulnerabilities to Byzantine attacks, where the Byzantine attackers can send arbitrary gradients to a central server to destroy the convergence and…
DEJA VU: Continual Model Generalization For Unseen Domains
Chenxi Liu, Lixu Wang, Lingjuan Lyu +3
In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous…
FedSkip: Combatting Statistical Heterogeneity with Federated Skip Aggregation
Ziqing Fan, Yanfeng Wang, Jiangchao Yao +3
The statistical heterogeneity of the non-independent and identically distributed (non-IID) data in local clients significantly limits the performance of federated learning. Previou…
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals
Rui Song, Liguo Zhou, Lingjuan Lyu +2
Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. However, with increasing model size, deploying…
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling
Junyuan Hong, Lingjuan Lyu, Jiayu Zhou +1
As deep learning blooms with growing demand for computation and data resources, outsourcing model training to a powerful cloud server becomes an attractive alternative to training…
Accelerated Federated Learning with Decoupled Adaptive Optimization
Jiayin Jin, Jiaxiang Ren, Yang Zhou +3
The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuri…
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Tao Qi, Fangzhao Wu, Chuhan Wu +5
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving w…
Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation
Chaochao Chen, Huiwen Wu, Jiajie Su +3
Cross Domain Recommendation (CDR) has been popularly studied to alleviate the cold-start and data sparsity problem commonly existed in recommender systems. CDR models can improve t…
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Ruixuan Liu, Fangzhao Wu, Chuhan Wu +4
Federated learning (FL) is an important paradigm for training global models from decentralized data in a privacy-preserving way. Existing FL methods usually assume the global model…
DENSE: Data-Free One-Shot Federated Learning
Jie Zhang, Chen Chen, Bo Li +5
One-shot Federated Learning (FL) has recently emerged as a promising approach, which allows the central server to learn a model in a single communication round. Despite the low com…
A Vertical Federated Learning Framework for Graph Convolutional Network
Xiang Ni, Xiaolong Xu, Lingjuan Lyu +2
Recently, Graph Neural Network (GNN) has achieved remarkable success in various real-world problems on graph data. However in most industries, data exists in the form of isolated i…
Towards Differentially Private Text Representations
Lingjuan Lyu, Yitong Li, Xuanli He +1
Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server wh…
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification
Chaochao Chen, Jun Zhou, Longfei Zheng +7
Recently, Graph Neural Network (GNN) has achieved remarkable progresses in various real-world tasks on graph data, consisting of node features and the adjacent information between…