7 citations · 26 across the 10 of their papers we have counts for
10 papers
A Framework for Incentivized Collaborative Learning
Xinran Wang, Qi Le, Ahmad Faraz Khan +2
Collaborations among various entities, such as companies, research labs, AI agents, and edge devices, have become increasingly crucial for achieving machine learning tasks that can…
Semi-Supervised Federated Learning for Keyword Spotting
Enmao Diao, Eric W. Tramel, Jie Ding +1
Keyword Spotting (KWS) is a critical aspect of audio-based applications on mobile devices and virtual assistants. Recent developments in Federated Learning (FL) have significantly…
Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging
Erum Mushtaq, Yavuz Faruk Bakman, Jie Ding +1
Federated Learning (FL) aims to train a machine learning (ML) model in a distributed fashion to strengthen data privacy with limited data migration costs. It is a distributed learn…
IP-FL: Incentivized and Personalized Federated Learning
Ahmad Faraz Khan, Xinran Wang, Qi Le +7
Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personaliza…
A Framework for Understanding Model Extraction Attack and Defense
Xun Xian, Mingyi Hong, Jie Ding
The privacy of machine learning models has become a significant concern in many emerging Machine-Learning-as-a-Service applications, where prediction services based on well-trained…
Self-Aware Personalized Federated Learning
Huili Chen, Jie Ding, Eric Tramel +4
In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives…
Federated Learning Challenges and Opportunities: An Outlook
Jie Ding, Eric Tramel, Anit Kumar Sahu +3
Federated learning (FL) has been developed as a promising framework to leverage the resources of edge devices, enhance customers' privacy, comply with regulations, and reduce devel…
SPIDER: Searching Personalized Neural Architecture for Federated Learning
Erum Mushtaq, Chaoyang He, Jie Ding +1
Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server due to privacy and regulat…
Subset Privacy: Draw from an Obfuscated Urn
Ganghua Wang, Jie Ding
With the rapidly increasing ability to collect and analyze personal data, data privacy becomes an emerging concern. In this work, we develop a new statistical notion of local priva…
The Rate of Convergence of Variation-Constrained Deep Neural Networks
Gen Li, Jie Ding
Multi-layer feedforward networks have been used to approximate a wide range of nonlinear functions. An important and fundamental problem is to understand the learnability of a netw…