4 papers
FedMM: Federated Collaborative Signal Quantization for Multi-Market CTR Prediction
Jun Zhang, Dugang Liu, Xing Tang +2
Online platforms such as Amazon and Netflix serve users across multiple countries and regions, underscoring the importance of multi-market recommendation (MMR). Most MMR methods ad…
Learn How to Query from Unlabeled Data Streams in Federated Learning
Yuchang Sun, Xinran Li, Tao Lin +1
Federated learning (FL) enables collaborative learning among decentralized clients while safeguarding the privacy of their local data. Existing studies on FL typically assume offli…
How to Collaborate: Towards Maximizing the Generalization Performance in Cross-Silo Federated Learning
Yuchang Sun, Marios Kountouris, Jun Zhang
Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed learning framework. In this work, we focus on cross-silo FL, where clients become the mode…
Exploring Selective Layer Fine-Tuning in Federated Learning
Yuchang Sun, Yuexiang Xie, Bolin Ding +2
Federated learning (FL) has emerged as a promising paradigm for fine-tuning foundation models using distributed data in a privacy-preserving manner. Under limited computational res…