no code implementations • 14 Apr 2024 • Yu Qiao, Huy Q. Le, Mengchun Zhang, Apurba Adhikary, Chaoning Zhang, Choong Seon Hong
First, we employ clustering on the local representations of each client, aiming to capture intra-class information based on these local clusters at a high level of granularity.
no code implementations • 10 Apr 2024 • Yu Qiao, Chaoning Zhang, Apurba Adhikary, Choong Seon Hong
Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks.
no code implementations • 8 Mar 2024 • Joseph Cho, Fachrina Dewi Puspitasari, Sheng Zheng, Jingyao Zheng, Lik-Hang Lee, Tae-Ho Kim, Choong Seon Hong, Chaoning Zhang
Text-to-video generation marks a significant frontier in the rapidly evolving domain of generative AI, integrating advancements in text-to-image synthesis, video captioning, and text-guided editing.
no code implementations • 5 Mar 2024 • Yu Qiao, Apurba Adhikary, Chaoning Zhang, Choong Seon Hong
Meanwhile, the non-independent and identically distributed (non-IID) challenge of data distribution between edge devices can further degrade the performance of models.
no code implementations • 25 Jan 2024 • Huy Q. Le, Chu Myaet Thwal, Yu Qiao, Ye Lin Tun, Minh N. H. Nguyen, Choong Seon Hong
In this paper, we propose Multimodal Federated Cross Prototype Learning (MFCPL), a novel approach for MFL under severely missing modalities by conducting the complete prototypes to provide diverse modality knowledge in modality-shared level with the cross-modal regularization and modality-specific level with cross-modal contrastive mechanism.
no code implementations • 22 Jan 2024 • Ye Lin Tun, Chu Myaet Thwal, Le Quang Huy, Minh N. H. Nguyen, Choong Seon Hong
In a pure layer-wise training scheme, training one layer at a time may limit effective interaction between different layers of the model.
no code implementations • 22 Jan 2024 • Chu Myaet Thwal, Kyi Thar, Ye Lin Tun, Choong Seon Hong
Thus, our objective is to provide a personalized clinical decision support system with evolvable characteristics that can deliver accurate solutions and assist healthcare professionals in medical diagnosing.
no code implementations • 22 Jan 2024 • Chu Myaet Thwal, Ye Lin Tun, Kitae Kim, Seong-Bae Park, Choong Seon Hong
Recent innovations in transformers have shown their superior performance in natural language processing (NLP) and computer vision (CV).
no code implementations • 22 Jan 2024 • Chu Myaet Thwal, Minh N. H. Nguyen, Ye Lin Tun, Seong Tae Kim, My T. Thai, Choong Seon Hong
Federated learning (FL) has emerged as a promising approach to collaboratively train machine learning models across multiple edge devices while preserving privacy.
1 code implementation • 15 Dec 2023 • Chaoning Zhang, Dongshen Han, Sheng Zheng, Jinwoo Choi, Tae-Ho Kim, Choong Seon Hong
The efficiency bottleneck of SegEvery with SAM, however, lies in its mask decoder because it needs to first generate numerous masks with redundant grid-search prompts and then perform filtering to obtain the final valid masks.
no code implementations • 28 Nov 2023 • Ye Lin Tun, Minh N. H. Nguyen, Chu Myaet Thwal, Jinwoo Choi, Choong Seon Hong
Together, self-supervised pre-training and client clustering can be crucial components for tackling the data heterogeneity issues of FL.
1 code implementation • 28 Nov 2023 • Ye Lin Tun, Chu Myaet Thwal, Ji Su Yoon, Sun Moo Kang, Chaoning Zhang, Choong Seon Hong
We conduct experiments on various FL scenarios, and our findings demonstrate that federated diffusion models have great potential to deliver vision services to privacy-sensitive domains.
no code implementations • 15 Nov 2023 • Yu Min Park, Yan Kyaw Tun, Choong Seon Hong
Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment.
no code implementations • 20 Oct 2023 • Loc X. Nguyen, Huy Q. Le, Ye Lin Tun, Pyae Sone Aung, Yan Kyaw Tun, Zhu Han, Choong Seon Hong
Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy.
no code implementations • 27 Sep 2023 • Long Tan Le, Tuan Dung Nguyen, Tung-Anh Nguyen, Choong Seon Hong, Nguyen H. Tran
Federated Learning (FL) is a prominent distributed learning paradigm facilitating collaboration among nodes within an edge network to co-train a global model without centralizing data.
no code implementations • 23 Sep 2023 • Christo Kurisummoottil Thomas, Christina Chaccour, Walid Saad, Merouane Debbah, Choong Seon Hong
We showcase how incorporating causal discovery can assist in achieving dynamic adaptability, resilience, and cognition in addressing these challenges.
no code implementations • ICCV 2023 • Quang Hieu Vo, Linh-Tam Tran, Sung-Ho Bae, Lok-Won Kim, Choong Seon Hong
Binary neural networks (BNNs) have been widely adopted to reduce the computational cost and memory storage on edge-computing devices by using one-bit representation for activations and weights.
no code implementations • 25 Jul 2023 • Huy Q. Le, Minh N. H. Nguyen, Chu Myaet Thwal, Yu Qiao, Chaoning Zhang, Choong Seon Hong
Bringing this concept into a system, we develop a distillation-based multimodal embedding knowledge transfer mechanism, namely FedMEKT, which allows the server and clients to exchange the joint knowledge of their learning models extracted from a small multimodal proxy dataset.
no code implementations • 20 Jul 2023 • Yu Qiao, Huy Q. Le, Choong Seon Hong
As a distributed machine learning technique, federated learning (FL) requires clients to collaboratively train a shared model with an edge server without leaking their local data.
no code implementations • 5 Jul 2023 • Tengchan Zeng, Aidin Ferdowsi, Omid Semiari, Walid Saad, Choong Seon Hong
For both cases, solutions using the convergence of communication theory, control theory, and machine learning are proposed to enable effective and secure CAV navigation.
2 code implementations • 25 Jun 2023 • Chaoning Zhang, Dongshen Han, Yu Qiao, Jung Uk Kim, Sung-Ho Bae, Seungkyu Lee, Choong Seon Hong
Concretely, we distill the knowledge from the heavy image encoder (ViT-H in the original SAM) to a lightweight image encoder, which can be automatically compatible with the mask decoder in the original SAM.
no code implementations • 13 Jun 2023 • Yu Qiao, Chaoning Zhang, Taegoo Kang, Donghun Kim, Chenshuang Zhang, Choong Seon Hong
Following by interpreting the effects of synthetic corruption as style changes, we proceed to conduct a comprehensive evaluation for its robustness against 15 types of common corruption.
no code implementations • 3 Jun 2023 • Chaoning Zhang, Yu Qiao, Shehbaz Tariq, Sheng Zheng, Chenshuang Zhang, Chenghao Li, Hyundong Shin, Choong Seon Hong
Different from label-oriented recognition tasks, the SAM is trained to predict a mask for covering the object shape based on a promt.
no code implementations • 12 May 2023 • Chaoning Zhang, Fachrina Dewi Puspitasari, Sheng Zheng, Chenghao Li, Yu Qiao, Taegoo Kang, Xinru Shan, Chenshuang Zhang, Caiyan Qin, Francois Rameau, Lik-Hang Lee, Sung-Ho Bae, Choong Seon Hong
This is an ongoing project and we intend to update the manuscript on a regular basis.
no code implementations • 10 May 2023 • Chenghao Li, Chaoning Zhang, Atish Waghwase, Lik-Hang Lee, Francois Rameau, Yang Yang, Sung-Ho Bae, Choong Seon Hong
AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC.
no code implementations • 29 Apr 2023 • Dongsheng Han, Chaoning Zhang, Yu Qiao, Maryam Qamar, Yuna Jung, Seungkyu Lee, Sung-Ho Bae, Choong Seon Hong
Meta AI Research has recently released SAM (Segment Anything Model) which is trained on a large segmentation dataset of over 1 billion masks.
no code implementations • 4 Apr 2023 • Chaoning Zhang, Chenshuang Zhang, Chenghao Li, Yu Qiao, Sheng Zheng, Sumit Kumar Dam, Mengchun Zhang, Jung Uk Kim, Seong Tae Kim, Jinwoo Choi, Gyeong-Moon Park, Sung-Ho Bae, Lik-Hang Lee, Pan Hui, In So Kweon, Choong Seon Hong
Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges.
no code implementations • 1 Apr 2023 • Yu Qiao, Md. Shirajum Munir, Apurba Adhikary, Huy Q. Le, Avi Deb Raha, Chaoning Zhang, Choong Seon Hong
The existing single prototype-based strategy represents a class by using the mean of the feature space.
no code implementations • 22 Mar 2023 • Yu Qiao, Seong-Bae Park, Sun Moo Kang, Choong Seon Hong
In this paper, a prototype-based federated learning framework is proposed, which can achieve better inference performance with only a few changes to the last global iteration of the typical federated learning process.
no code implementations • 21 Mar 2023 • Chaoning Zhang, Chenshuang Zhang, Sheng Zheng, Yu Qiao, Chenghao Li, Mengchun Zhang, Sumit Kumar Dam, Chu Myaet Thwal, Ye Lin Tun, Le Luang Huy, Donguk Kim, Sung-Ho Bae, Lik-Hang Lee, Yang Yang, Heng Tao Shen, In So Kweon, Choong Seon Hong
As ChatGPT goes viral, generative AI (AIGC, a. k. a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond.
1 code implementation • 28 Oct 2022 • Ye Lin Tun, Chu Myaet Thwal, Yu Min Park, Seong-Bae Park, Choong Seon Hong
Specifically, FedIntR computes a regularization term that encourages the closeness between the intermediate layer representations of the local and global models.
no code implementations • 28 Oct 2022 • Ye Lin Tun, Kyi Thar, Chu Myaet Thwal, Choong Seon Hong
In this paper, we propose a recurrent neural network based energy demand predictor, trained with federated learning on clustered clients to take advantage of distributed data and speed up the convergence process.
no code implementations • 13 Oct 2022 • Md. Shirajum Munir, Ki Tae Kim, Apurba Adhikary, Walid Saad, Sachin Shetty, Seong-Bae Park, Choong Seon Hong
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2
no code implementations • 4 Apr 2022 • Minh N. H. Nguyen, Huy Q. Le, Shashi Raj Pandey, Choong Seon Hong
Therefore, to develop robust generalized global and personalized models, conventional FL methods need redesigning the knowledge aggregation from biased local models while considering huge divergence of learning parameters due to skewed client data.
no code implementations • 26 Jan 2022 • Md. Shirajum Munir, Seong-Bae Park, Choong Seon Hong
First, a problem of quality-aware IoE service delivery is formulated by taking into account network dynamics and contextual metrics of IoE, where the objective is to maximize the channel quality index (CQI) of each IoE service user.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
no code implementations • 2 Aug 2021 • Md. Shirajum Munir, Ki Tae Kim, Kyi Thar, Dusit Niyato, Choong Seon Hong
To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO.
no code implementations • 31 Mar 2021 • Mehdi Naderi Soorki, Walid Saad, Mehdi Bennis, Choong Seon Hong
Simulation results show that the error between policies of the optimal and the RNN-based controllers is less than 1. 5%.
no code implementations • 24 Feb 2021 • Latif U. Khan, Walid Saad, Dusit Niyato, Zhu Han, Choong Seon Hong
Therefore, enabling IoE applications over 6G requires a new framework that can be used to manage, operate, and optimize the 6G wireless system and its underlying IoE services.
Edge-computing Networking and Internet Architecture
no code implementations • 1 Dec 2020 • Shashi Raj Pandey, Minh N. H. Nguyen, Tri Nguyen Dang, Nguyen H. Tran, Kyi Thar, Zhu Han, Choong Seon Hong
Therefore, we need to design a robust learning mechanism than the FL that (i) unleashes a viable infrastructure for FA and (ii) trains learning models with better generalization capability.
1 code implementation • 25 Nov 2020 • Minh N. H. Nguyen, Nguyen H. Tran, Yan Kyaw Tun, Zhu Han, Choong Seon Hong
Federated Learning is a new learning scheme for collaborative training a shared prediction model while keeping data locally on participating devices.
no code implementations • 28 Sep 2020 • Latif U. Khan, Walid Saad, Zhu Han, Ekram Hossain, Choong Seon Hong
However, given the presence of massively distributed and private datasets, it is challenging to use classical centralized learning algorithms in the IoT.
Networking and Internet Architecture
no code implementations • 22 Sep 2020 • Tra Huong Thi Le, Nguyen H. Tran, Yan Kyaw Tun, Minh N. H. Nguyen, Shashi Raj Pandey, Zhu Han, Choong Seon Hong
In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users.
no code implementations • 24 Aug 2020 • Md. Shirajum Munir, Sarder Fakhrul Abedin, Ki Tae Kim, Do Hyeon Kim, Md. Golam Rabiul Alam, Choong Seon Hong
Further, we develop a prototype of the proposed drive safe platform to establish proof-of-concept (PoC) for the road safety in IT-CPS.
no code implementations • 12 Aug 2020 • Latif U. Khan, Walid Saad, Zhu Han, Choong Seon Hong
However, federated learning still has privacy concerns due to sensitive information inferring capability of the aggregation server using end-devices local learning models.
Distributed, Parallel, and Cluster Computing
1 code implementation • 7 Jul 2020 • Minh N. H. Nguyen, Shashi Raj Pandey, Tri Nguyen Dang, Eui-Nam Huh, Nguyen H. Tran, Walid Saad, Choong Seon Hong
Inspired by Dem-AI philosophy, a novel distributed learning approach is proposed in this paper.
no code implementations • 5 Jul 2020 • Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, Mohammad Shikh-Bahaei, H. Vincent Poor, Shuguang Cui
In this paper, the problem of delay minimization for federated learning (FL) over wireless communication networks is investigated.
no code implementations • 19 Mar 2020 • Sihua Wang, Mingzhe Chen, Changchuan Yin, Walid Saad, Choong Seon Hong, Shuguang Cui, H. Vincent Poor
This problem is posed as an optimization problem whose goal is to minimize the energy and time consumption for task computing and transmission by adjusting the user association, service sequence, and task allocation scheme.
1 code implementation • 18 Mar 2020 • Minh N. H. Nguyen, Shashi Raj Pandey, Kyi Thar, Nguyen H. Tran, Mingzhe Chen, Walid Saad, Choong Seon Hong
Consequently, many emerging cross-device AI applications will require a transition from traditional centralized learning systems towards large-scale distributed AI systems that can collaboratively perform multiple complex learning tasks.
no code implementations • 21 Feb 2020 • Sarder Fakhrul Abedin, Md. Shirajum Munir, Nguyen H. Tran, Zhu Han, Choong Seon Hong
First, we formulate an energy-efficient trajectory optimization problem in which the objective is to maximize the energy efficiency by optimizing the UAV-BS trajectory policy.
no code implementations • 21 Feb 2020 • Md. Shirajum Munir, Sarder Fakhrul Abedin, Nguyen H. Tran, Zhu Han, Eui-Nam Huh, Choong Seon Hong
First, we formulate an optimization problem considering the conditional value-at-risk (CVaR) measurement for both energy consumption and generation, where the objective is to minimize the expected residual of scheduled energy for the MEC networks and we show this problem is an NP-hard problem.
no code implementations • 20 Feb 2020 • Md. Shirajum Munir, Nguyen H. Tran, Walid Saad, Choong Seon Hong
In particular, each BS plays the role of a local agent that explores a Markovian behavior for both energy consumption and generation while each BS transfers time-varying features to a meta-agent.
no code implementations • 22 Jan 2020 • Hamza Khan, Anis Elgabli, Sumudu Samarakoon, Mehdi Bennis, Choong Seon Hong
Vehicle-to-everything (V2X) communication is a growing area of communication with a variety of use cases.
no code implementations • 15 Nov 2019 • Soheil Rostami, Walid Saad, Choong Seon Hong
To maintain lower error rate in presence of severe atmospheric turbulence, a new approach that combines effective machine learning tools from persistent homology and convolutional neural networks (CNNs) is proposed to decode the OAM modes.
no code implementations • 13 Nov 2019 • Madyan Alsenwi, Kitae Kim, Choong Seon Hong
In this paper, we address the problem of RBs allocation to UEs.
no code implementations • 6 Nov 2019 • Latif U. Khan, Nguyen H. Tran, Shashi Raj Pandey, Walid Saad, Zhu Han, Minh N. H. Nguyen, Choong Seon Hong
IoT devices with intelligence require the use of effective machine learning paradigms.
Distributed, Parallel, and Cluster Computing
no code implementations • 6 Nov 2019 • Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, Mohammad Shikh-Bahaei
To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived.
no code implementations • 4 Nov 2019 • Shashi Raj Pandey, Nguyen H. Tran, Mehdi Bennis, Yan Kyaw Tun, Aunas Manzoor, Choong Seon Hong
Federated learning (FL) rests on the notion of training a global model in a decentralized manner.
4 code implementations • 29 Oct 2019 • Canh T. Dinh, Nguyen H. Tran, Minh N. H. Nguyen, Choong Seon Hong, Wei Bao, Albert Y. Zomaya, Vincent Gramoli
There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.
no code implementations • 2 Oct 2019 • Minchae Jung, Walid Saad, Merouane Debbah, Choong Seon Hong
In this paper, the asymptotic optimality of achievable rate in a downlink RIS system is analyzed under a practical RIS environment with its associated limitations.
Information Theory Signal Processing Information Theory