Search Results for author: Zehong Lin

Found 9 papers, 1 papers with code

Spatial-Aware Latent Initialization for Controllable Image Generation

no code implementations29 Jan 2024 Wenqiang Sun, Teng Li, Zehong Lin, Jun Zhang

Recently, text-to-image diffusion models have demonstrated impressive ability to generate high-quality images conditioned on the textual input.

Denoising Image Generation

FedCiR: Client-Invariant Representation Learning for Federated Non-IID Features

no code implementations30 Aug 2023 Zijian Li, Zehong Lin, Jiawei Shao, Yuyi Mao, Jun Zhang

However, devices often have non-independent and identically distributed (non-IID) data, meaning their local data distributions can vary significantly.

Federated Learning Representation Learning

Large Language Models Empowered Autonomous Edge AI for Connected Intelligence

no code implementations6 Jul 2023 Yifei Shen, Jiawei Shao, Xinjie Zhang, Zehong Lin, Hao Pan, Dongsheng Li, Jun Zhang, Khaled B. Letaief

The evolution of wireless networks gravitates towards connected intelligence, a concept that envisions seamless interconnectivity among humans, objects, and intelligence in a hyper-connected cyber-physical world.

Code Generation Federated Learning +3

Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning

no code implementations26 May 2023 Yuchang Sun, Zehong Lin, Yuyi Mao, Shi Jin, Jun Zhang

In this paper, we propose a probabilistic device scheduling framework for over-the-air FL, named PO-FL, to mitigate the negative impact of channel noise, where each device is scheduled according to a certain probability and its model update is reweighted using this probability in aggregation.

Federated Learning Privacy Preserving +1

Understanding and Improving Model Averaging in Federated Learning on Heterogeneous Data

no code implementations13 May 2023 Tailin Zhou, Zehong Lin, Jun Zhang, Danny H. K. Tsang

Based on these findings from our loss landscape visualization and loss decomposition, we propose utilizing iterative moving averaging (IMA) on the global model at the late training phase to reduce its deviation from the expected minimum, while constraining client exploration to limit the maximum distance between the global and client models.

Federated Learning

CFLIT: Coexisting Federated Learning and Information Transfer

no code implementations26 Jul 2022 Zehong Lin, Hang Liu, Ying-Jun Angela Zhang

We propose a coexisting federated learning and information transfer (CFLIT) communication framework, where the FL and IT devices share the wireless spectrum in an OFDM system.

Federated Learning

Reconfigurable Intelligent Surface Empowered Over-the-Air Federated Edge Learning

no code implementations6 Sep 2021 Hang Liu, Zehong Lin, Xiaojun Yuan, Ying-Jun Angela Zhang

Federated edge learning (FEEL) has emerged as a revolutionary paradigm to develop AI services at the edge of 6G wireless networks as it supports collaborative model training at a massive number of mobile devices.

Relay-Assisted Cooperative Federated Learning

1 code implementation20 Jul 2021 Zehong Lin, Hang Liu, Ying-Jun Angela Zhang

Then, we analyze the model aggregation error in a single-relay case and show that our relay-assisted scheme achieves a smaller error than the one without relays provided that the relay transmit power and the relay channel gains are sufficiently large.

Federated Learning

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