no code implementations • 2 May 2024 • Huancheng Chen, Haris Vikalo
Gradient inversion (GI) attacks present a threat to the privacy of clients in federated learning (FL) by aiming to enable reconstruction of the clients' data from communicated model updates.
no code implementations • 29 Nov 2023 • Huancheng Chen, Haris Vikalo
While federated learning (FL) systems often utilize quantization to battle communication and computational bottlenecks, they have heretofore been limited to deploying fixed-precision quantization schemes.
no code implementations • 30 Sep 2023 • Huancheng Chen, Haris Vikalo
Statistical heterogeneity of data present at client devices in a federated learning (FL) system renders the training of a global model in such systems difficult.
no code implementations • 21 Jan 2023 • Huancheng Chen, Johnny, Wang, Haris Vikalo
In particular, each client extracts and sends to the server the means of local data representations and the corresponding soft predictions -- information that we refer to as ``hyper-knowledge".
1 code implementation • 1 Jun 2022 • Huancheng Chen, Haris Vikalo
A major challenge in federated learning arises when the local data is heterogeneous -- the setting in which performance of the learned global model may deteriorate significantly compared to the scenario where the data is identically distributed across the clients.
1 code implementation • 21 Sep 2021 • Abduallah Mohamed, Huancheng Chen, Zhangyang Wang, Christian Claudel
We propose Skeleton-Graph, a deep spatio-temporal graph CNN model that predicts the future 3D skeleton poses in a single pass from the 2D ones.
Ranked #1 on Trajectory Prediction on PROX