33 citations · 109 across the 39 of their papers we have counts for
5 papers · 1 filter
Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning
Chih Wei Ling, Chun Hei Michael Shiu, Youqi Wu +4
Training machine learning models on decentralized private data via federated learning (FL) poses two key challenges: communication efficiency and privacy protection. In this work,…
Be More Diverse than the Most Diverse: Optimal Mixtures of Generative Models via Mixture-UCB Bandit Algorithms
Parham Rezaei, Farzan Farnia, Cheuk Ting Li
The availability of multiple training algorithms and architectures for generative models requires a selection mechanism to form a single model over a group of well-trained generati…
Unveiling Differences in Generative Models: A Scalable Differential Clustering Approach
Jingwei Zhang, Mohammad Jalali, Cheuk Ting Li +1
A fine-grained comparison of generative models requires the identification of sample types generated differently by each of the involved models. While quantitative scores have been…
An Interpretable Evaluation of Entropy-based Novelty of Generative Models
Jingwei Zhang, Cheuk Ting Li, Farzan Farnia
The massive developments of generative model frameworks require principled methods for the evaluation of a model's novelty compared to a reference dataset. While the literature has…
Compression with Exact Error Distribution for Federated Learning
Mahmoud Hegazy, Rémi Leluc, Cheuk Ting Li +1
Compression schemes have been extensively used in Federated Learning (FL) to reduce the communication cost of distributed learning. While most approaches rely on a bounded variance…