no code implementations • 9 Apr 2024 • Pengfei Zhang, Dingzhu Wen, Guangxu Zhu, Qimei Chen, Kaifeng Han, Yuanming Shi
To realize efficient uplink feature aggregation, we allow each RRH receives local feature vectors from all devices over the same resource blocks simultaneously by leveraging an over-the-air computation (AirComp) technique.
no code implementations • 15 Jan 2024 • Mingzhao Guo, Dongzhu Liu, Osvaldo Simeone, Dingzhu Wen
This paper presents a novel approach to enhance the communication efficiency of federated learning (FL) in multiple input and multiple output (MIMO) wireless systems.
no code implementations • 1 Jun 2023 • Dingzhu Wen, Xiaoyang Li, Yong Zhou, Yuanming Shi, Sheng Wu, Chunxiao Jiang
Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything.
no code implementations • 2 Nov 2022 • Dingzhu Wen, Xiang Jiao, Peixi Liu, Guangxu Zhu, Yuanming Shi, Kaibin Huang
To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy.
no code implementations • 3 Jul 2022 • Dingzhu Wen, Peixi Liu, Guangxu Zhu, Yuanming Shi, Jie Xu, Yonina C. Eldar, Shuguang Cui
This paper studies a new multi-device edge artificial-intelligent (AI) system, which jointly exploits the AI model split inference and integrated sensing and communication (ISAC) to enable low-latency intelligent services at the network edge.
no code implementations • 30 Sep 2021 • Dingzhu Wen, Ki-Jun Jeon, Kaibin Huang
To tackle the challenge, in this paper, a federated dropout (FedDrop) scheme is proposed building on the classic dropout scheme for random model pruning.
no code implementations • 8 Oct 2020 • Dingzhu Wen, Ki-Jun Jeon, Mehdi Bennis, Kaibin Huang
Targeting broadband channels, we consider the joint control of parameter allocation, sub-channel allocation, and transmission power to improve the performance of PARTEL.
no code implementations • 1 Apr 2020 • Jinke Ren, Yinghui He, Dingzhu Wen, Guanding Yu, Kaibin Huang, Dongning Guo
In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates.
no code implementations • 10 Mar 2020 • Dingzhu Wen, Mehdi Bennis, Kaibin Huang
To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets.
no code implementations • 10 Nov 2019 • Dingzhu Wen, Xiaoyang Li, Qunsong Zeng, Jinke Ren, Kaibin Huang
Specifically, the metrics that measure data importance in active learning (e. g., classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers.