302 citations · 695 across the 16 of their papers we have counts for
18 papers
Learning to Sync at the Edge: Client-Side Adaptive Synchronization for Federated Learning
Ebasa Temesgen, Ammar Ahmed, Nathan Lechamo +2
The scalability and performance of edge federated learning are fundamentally constrained by synchronization bottlenecks. Clients operate under heterogeneous compute, time-varying c…
ChartRevise: A Dataset and Evaluation Protocol for Exact Chart Editing via Code
Jiaxiang Tang, Yi Zhou, Chad DeLuca +5
Chart editing requires cross-modal edit grounding, realizing a requested visual change in the code that draws it, with necessary related updates and without altering unrelated cont…
Personalized Federated Recommender Systems with Private and Partially Federated AutoEncoders
Qi Le, Enmao Diao, Xinran Wang +3
Recommender Systems (RSs) have become increasingly important in many application domains, such as digital marketing. Conventional RSs often need to collect users' data, centralize…
DeTrust-FL: Privacy-Preserving Federated Learning in Decentralized Trust Setting
Runhua Xu, Nathalie Baracaldo, Yi Zhou +3
Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated…
Towards cost-effective and resource-aware aggregation at Edge for Federated Learning
Ahmad Faraz Khan, Yuze Li, Xinran Wang +5
Federated Learning (FL) is a machine learning approach that addresses privacy and data transfer costs by computing data at the source. It's particularly popular for Edge and IoT ap…
SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
Sixing Yu, Phuong Nguyen, Waqwoya Abebe +3
Federated learning~(FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive comm…