3 papers
cs.LG2023
Knowledge Rumination for Client Utility Evaluation in Heterogeneous Federated Learning
Xiaorui Jiang, Yu Gao, Hengwei Xu +3
Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data. In practical applications, asynchronous FL (AFL) can…
cs.DC2022
Celeritas: Fast Optimizer for Large Dataflow Graphs
Hengwei Xu, Yong Liao, Haiyong Xie +1
The rapidly enlarging neural network models are becoming increasingly challenging to run on a single device. Hence model parallelism over multiple devices is critical to guarantee…
cs.LG2022
HideNseek: Federated Lottery Ticket via Server-side Pruning and Sign Supermask
Anish K. Vallapuram, Pengyuan Zhou, Young D. Kwon +3
Federated learning alleviates the privacy risk in distributed learning by transmitting only the local model updates to the central server. However, it faces challenges including st…