12 citations · 25 across the 9 of their papers we have counts for
9 papers
FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching
Qifan Yan, Andrew Liu, Shiqi He +2
Federated learning (FL) is a machine learning paradigm that facilitates massively distributed model training with end-user data on edge devices directed by a central server. Howeve…
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning
Zhifeng Jiang, Peng Ye, Shiqi He +3
In Federated Learning (FL), common privacy-enhancing techniques, such as secure aggregation and distributed differential privacy, rely on the critical assumption of an honest major…
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
Rindranirina Ramamonjison, Timothy T. Yu, Raymond Li +8
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text…
GlueFL: Reconciling Client Sampling and Model Masking for Bandwidth Efficient Federated Learning
Shiqi He, Qifan Yan, Feijie Wu +3
Federated learning (FL) is an effective technique to directly involve edge devices in machine learning training while preserving client privacy. However, the substantial communicat…
Augmenting Operations Research with Auto-Formulation of Optimization Models from Problem Descriptions
Rindranirina Ramamonjison, Haley Li, Timothy T. Yu +5
We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formula…
Anchor Sampling for Federated Learning with Partial Client Participation
Feijie Wu, Song Guo, Zhihao Qu +3
Compared with full client participation, partial client participation is a more practical scenario in federated learning, but it may amplify some challenges in federated learning,…
Sign Bit is Enough: A Learning Synchronization Framework for Multi-hop All-reduce with Ultimate Compression
Feijie Wu, Shiqi He, Song Guo +4
Traditional one-bit compressed stochastic gradient descent can not be directly employed in multi-hop all-reduce, a widely adopted distributed training paradigm in network-intensive…
Stability analysis of generalized Lugiato-Lefever equation with lumped filter for Kerr optical soliton generation in anomalous dispersion regime
Nuo Chen, Boqing Zhang, Haofan Yang +6
We raise a detuning-dependent loss mechanism to describe the soliton formation dynamics when the lumped filtering operation is manipulated in anomalous group velocity dispersion re…
On the Convergence of Quantized Parallel Restarted SGD for Central Server Free Distributed Training
Feijie Wu, Shiqi He, Yutong Yang +4
Communication is a crucial phase in the context of distributed training. Because parameter server (PS) frequently experiences network congestion, recent studies have found that tra…