359 citations · 383 across the 6 of their papers we have counts for
6 papers
Multi-Job Intelligent Scheduling with Cross-Device Federated Learning
Ji Liu, Juncheng Jia, Beichen Ma +5
Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine le…
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…
Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothing
Jiayin Jin, Zeru Zhang, Yang Zhou +1
Only recently, researchers attempt to provide classification algorithms with provable group fairness guarantees. Most of these algorithms suffer from harassment caused by the requi…
Efficient Device Scheduling with Multi-Job Federated Learning
Chendi Zhou, Ji Liu, Juncheng Jia +4
Recent years have witnessed a large amount of decentralized data in multiple (edge) devices of end-users, while the aggregation of the decentralized data remains difficult for mach…
Cross-lingual Entity Alignment with Adversarial Kernel Embedding and Adversarial Knowledge Translation
Gong Zhang, Yang Zhou, Sixing Wu +2
Cross-lingual entity alignment, which aims to precisely connect the same entities in different monolingual knowledge bases (KBs) together, often suffers challenges from feature inc…
From Distributed Machine Learning to Federated Learning: A Survey
Ji Liu, Jizhou Huang, Yang Zhou +4
In recent years, data and computing resources are typically distributed in the devices of end users, various regions or organizations. Because of laws or regulations, the distribut…