84 citations · 223 across the 25 of their papers we have counts for
4 papers · 1 filter
Auxo: Efficient Federated Learning via Scalable Client Clustering
Jiachen Liu, Fan Lai, Yinwei Dai +3
Federated learning (FL) is an emerging machine learning (ML) paradigm that enables heterogeneous edge devices to collaboratively train ML models without revealing their raw data to…
Shockwave: Fair and Efficient Cluster Scheduling for Dynamic Adaptation in Machine Learning
Pengfei Zheng, Rui Pan, Tarannum Khan +2
Dynamic adaptation has become an essential technique in accelerating distributed machine learning (ML) training. Recent studies have shown that dynamically adjusting model structur…
Impact of RoCE Congestion Control Policies on Distributed Training of DNNs
Tarannum Khan, Saeed Rashidi, Srinivas Sridharan +3
RDMA over Converged Ethernet (RoCE) has gained significant attraction for datacenter networks due to its compatibility with conventional Ethernet-based fabric. However, the RDMA pr…
Elastic Model Aggregation with Parameter Service
Juncheng Gu, Mosharaf Chowdhury, Kang G. Shin +1
Model aggregation, the process that updates model parameters, is an important step for model convergence in distributed deep learning (DDL). However, the parameter server (PS), a p…