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[ACM MobiCom 2022] "PyramidFL: Fine-grained Data and System Heterogeneity-aware Client Selection for Efficient Federated Learning" by Chenning Li, Xiao Zeng, Mi Zhang, and Zhichao Cao.
A principled library for tuning, training and evaluating tabular data synthesis on fidelity, privacy and utility. CCS 2025.
THC: Accelerating Distributed Deep Learning Using Tensor Homomorphic Compression
The code for the paper "QuAFL: Federated Averaging Can Be Both Asynchronous and Communication-Efficient"
The repository for ATC'25 paper "Greyhound: Hunting Fail-Slows in Hybrid-Parallel Training at Scale"
A prefetching technique for faster federated learning