22 citations · 64 across the 34 of their papers we have counts for
6 papers · 2 filters
BGTplanner: Maximizing Training Accuracy for Differentially Private Federated Recommenders via Strategic Privacy Budget Allocation
Xianzhi Zhang, Yipeng Zhou, Miao Hu +4
To mitigate the rising concern about privacy leakage, the federated recommender (FR) paradigm emerges, in which decentralized clients co-train the recommendation model without expo…
A Communication and Computation Efficient Fully First-order Method for Decentralized Bilevel Optimization
Min Wen, Chengchang Liu, Ahmed Abdelmoniem +2
Bilevel optimization, crucial for hyperparameter tuning, meta-learning and reinforcement learning, remains less explored in the decentralized learning paradigm, such as decentraliz…
The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
Jiating Ma, Yipeng Zhou, Qi Li +3
To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To…
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
Huitong Jin, Yipeng Zhou, Quan Z. Sheng +2
Differentially Private Federated Learning (DPFL) strengthens privacy protection by perturbing model gradients with noise, though at the cost of reduced accuracy. Although prior emp…
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
Xiaoxin Su, Yipeng Zhou, Laizhong Cui +1
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates…
Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
Xiaoxin Su, Yipeng Zhou, Laizhong Cui +2
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model di…