302 citations · 695 across the 16 of their papers we have counts for
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cs.IR2022
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…
cs.CR2022
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…
cs.LG2022★ 7 cited
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…