BiTAT: Neural Network Binarization with Task-dependent Aggregated Transformation

no code implementations • 4 Jul 2022 • Geon Park, Jaehong Yoon, Haiyang Zhang, Xing Zhang, Sung Ju Hwang, Yonina C. Eldar

Neural network quantization aims to transform high-precision weights and activations of a given neural network into low-precision weights/activations for reduced memory usage and computation, while preserving the performance of the original model.

Binarization Quantization

Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

no code implementations • 23 Feb 2022 • Jaehong Yoon, Geon Park, Wonyong Jeong, Sung Ju Hwang

We introduce a pragmatic FL scenario with bitwidth heterogeneity across the participating devices, dubbed as Bitwidth Heterogeneous Federated Learning (BHFL).

Federated Learning