2 citations · 2 across the 1 of their papers we have counts for
4 papers
DεpS: Delayed ε-Shrinking for Faster Once-For-All Training
Aditya Annavajjala, Alind Khare, Animesh Agrawal +4
CNNs are increasingly deployed across different hardware, dynamic environments, and low-power embedded devices. This has led to the design and training of CNN architectures with th…
FedAuxHMTL: Federated Auxiliary Hard-Parameter Sharing Multi-Task Learning for Network Edge Traffic Classification
Faisal Ahmed, Myungjin Lee, Suresh Subramaniam +3
Federated Learning (FL) has garnered significant interest recently due to its potential as an effective solution for tackling many challenges in diverse application scenarios, for…
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
Gustav A. Baumgart, Jaemin Shin, Ali Payani +2
Federated Learning (FL) emerged as a practical approach to training a model from decentralized data. The proliferation of FL led to the development of numerous FL algorithms and me…
Mitigating Group Bias in Federated Learning: Beyond Local Fairness
Ganghua Wang, Ali Payani, Myungjin Lee +1
The issue of group fairness in machine learning models, where certain sub-populations or groups are favored over others, has been recognized for some time. While many mitigation st…