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Dusit Niyato

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11 papers

1 author row

IS Journal 2026 Journal Article

A Survey on Continuous Unlearning in Generative AI: Approaches and Tradeoffs

Yang Zhao
Hongyang Du
Yijing Lin
Keyi Xiang
Dusit Niyato
H. Vincent Poor

Generative artificial intelligence (GenAI) models have innovated content creation but raise concerns about privacy, security, and regulatory compliance such as General Data Protection Regulation. In response, unlearning techniques have emerged to selectively remove data while preserving the utility of the model. This article reviews unlearning methods in centralized and decentralized settings. These strategies mitigate risks, such as data leakage, membership inference, and bias amplification. By integrating unlearning with continuous or lifelong learning paradigms, GenAI models can adapt dynamically while honoring the “right to be forgotten. ” In existing unlearning methods, we explore key tradeoffs involving computational overhead, accuracy retention, generative quality, and thorough data deletion. Our review covers technical and ethical considerations and future directions, highlighting a balanced path toward responsible GenAI systems.

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TMLR Journal 2026 Journal Article

Incorporating New Knowledge into Federated Learning: Advances, Insights, and Future Directions

Lixu Wang
Sun Yinggang
Yang Zhao
Jiaqi Wu
Jiahua Dong
Ating Yin
Qinbin Li
Qingqing Ye

Federated Learning (FL) is a distributed learning approach that allows participants to collaboratively train machine learning models without sharing the raw data. It is rapidly developing in an era where privacy protection is increasingly valued. It is this rapid development trend, along with the continuous emergence of new demands for FL in the real world, that prompts us to focus on a very important problem: How to Incorporate New Knowledge into Federated Learning? The primary challenge here is to effectively and timely incorporate various new knowledge into existing FL systems and evolve these systems to reduce costs, upgrade functionalities, and facilitate sustainable development. In the meantime, established FL systems should preserve existing functionalities during the incorporation of new knowledge. In this paper, we systematically define the main sources of new knowledge in FL, including new features, tasks, models, and algorithms. For each source, we thoroughly analyze and discuss the technical approaches for incorporating new knowledge into existing FL systems and examine the impact of the form and timing of new knowledge arrival on the incorporation process. Unlike prior surveys that primarily catalogue FL techniques under a fixed system specification, we adopt a lifecycle evolution perspective and synthesize methods that enable time-varying integration of new features, tasks, models, and aggregation algorithms while preserving existing functionality. Furthermore, we comprehensively discuss the potential future directions for FL, incorporating new knowledge and considering a variety of factors, including scenario setups, security and privacy threats, and incentives.

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AAAI Conference 2025 Conference Paper

Supervised Score-Based Modeling by Gradient Boosting

Changyuan Zhao
Hongyang Du
Guangyuan Liu
Dusit Niyato

Score-based generative models can effectively learn the distribution of data by estimating the gradient of the distribution. Due to the multi-step denoising characteristic, researchers have recently considered combining score-based generative models with the gradient boosting algorithm, a multi-step supervised learning algorithm, to solve supervised learning tasks. However, existing generative model algorithms are often limited by the stochastic nature of the models and the long inference time, impacting prediction performances. Therefore, we propose a Supervised Score-based Model (SSM), which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Via the ablation experiment in selected examples, we demonstrate the outstanding performances of the proposed techniques. Additionally, we compare our model with other probabilistic models, including Natural Gradient Boosting (NGboost), Classification and Regression Diffusion Models (CARD), Diffusion Boosted Trees (DBT), and non-probabilistic gradient boosting models. The experimental results show that our model outperforms existing models in both accuracy and inference time.

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IJCAI Conference 2024 Conference Paper

Scalable Federated Unlearning via Isolated and Coded Sharding

Yijing Lin
Zhipeng Gao
Hongyang Du
Dusit Niyato
Gui Gui
Shuguang Cui
Jinke Ren

Federated unlearning has emerged as a promising paradigm to erase the client-level data effect without affecting the performance of collaborative learning models. However, the federated unlearning process often introduces extensive storage overhead and consumes substantial computational resources, thus hindering its implementation in practice. To address this issue, this paper proposes a scalable federated unlearning framework based on isolated sharding and coded computing. We first divide distributed clients into multiple isolated shards across stages to reduce the number of clients being affected. Then, to reduce the storage overhead of the central server, we develop a coded computing mechanism by compressing the model parameters across different shards. In addition, we provide the theoretical analysis of time efficiency and storage effectiveness for the isolated and coded sharding. Finally, extensive experiments on two typical learning tasks, i. e. , classification and generation, demonstrate that our proposed framework can achieve better performance than three state-of-the-art frameworks in terms of accuracy, retraining time, storage overhead, and F1 scores for resisting membership inference attacks.

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AAAI Conference 2022 System Paper

CrowdFL: A Marketplace for Crowdsourced Federated Learning

Daifei Feng
Cicilia Helena
Wei Yang Bryan Lim
Jer Shyuan Ng
Hongchao Jiang
Zehui Xiong
Jiawen Kang
Han Yu

Amid data privacy concerns, Federated Learning (FL) has emerged as a promising machine learning paradigm that enables privacy-preserving collaborative model training. However, there exists a need for a platform that matches data owners (supply) with model requesters (demand). In this paper, we present CrowdFL, a platform to facilitate the crowdsourcing of FL model training. It coordinates client selection, model training, and reputation management, which are essential steps for the FL crowdsourcing operations. By implementing model training on actual mobile devices, we demonstrate that the platform improves model performance and training efficiency. To the best of our knowledge, it is the first platform to support crowdsourcing-based FL on edge devices.

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AAAI Conference 2022 System Paper

Dynamic Incentive Mechanism Design for COVID-19 Social Distancing

Xuan Rong Zane Ho
Wei Yang Bryan Lim
Hongchao Jiang
Jer Shyuan Ng
Han Yu
Zehui Xiong
Dusit Niyato
Chunyan Miao

As countries enter the endemic phase of COVID-19, people’s risk of exposure to the virus is greater than ever. There is a need to make more informed decisions in our daily lives on avoiding crowded places. Crowd monitoring systems typically require costly infrastructure. We propose a crowdsourced crowd monitoring platform which leverages user inputs to generate crowd counts and forecast location crowdedness. A key challenge for crowd-sourcing is a lack of incentive for users to contribute. We propose a Reinforcement Learning based dynamic incentive mechanism to optimally allocate rewards to encourage user participation.

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AAMAS Conference 2021 Conference Paper

A Blockchain-Enabled Quantitative Approach to Trust and Reputation Management with Sparse Evidence

Leonit Zeynalvand
Tie Luo
Ewa Andrejczuk
Dusit Niyato
Sin G. Teo
Jie Zhang

The prevalence of e-commerce applications poses new trust challenges that render traditional Trust and Reputation Management (TRM) approaches inadequate. The first challenge is that TRM is built on evidence (direct or indirect observations) but evidence is becoming increasingly sparse because nowadays users have many more venues to share information. This makes it hard to derive trust models that are robust to attacks such as whitewashing and Sybil attacks. Second, the cost of attacks has reduced significantly due to the widespread presence of bots in e-commerce applications, which tends to invalidate the traditional assumption that majority users are honest. In this paper, we propose a new TRM framework called BEQA, which uses Blockchain to transform multiple disjoint and sparse sets of evidence into a single and dense evidence set. To address the second challenge, we introduce and formulate the cost of Sybil attacks using Blockchain transaction fees. In addition, we make a key observation that existing trust models have overlooked publicity (evidence originating from influencers) that exist in e-commerce applications. Thus, we formulate publicity as a whitewashing deposit such that a higher level of publicity will impose higher cost on Sybil attacks.

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AAAI Conference 2021 System Paper

AI-Empowered Decision Support for COVID-19 Social Distancing

Hongchao Jiang
Wei Yang Bryan Lim
Jer Shyuan Ng
Harold Ze Chie Teng
Han Yu
Zehui Xiong
Dusit Niyato
Chunyan Miao

The COVID-19 pandemic is one of the most severe challenges the world faces today. In order to contain the transmission of COVID-19, people around the world have been advised to practise social distancing. However, maintaining social distance is a challenging problem, as we often do not know beforehand how crowded the places we intend to visit are. In this paper, we demonstrate crowded. sg, an AIempowered platform that leverages on Unmanned Aerial Vehicles (UAVs), crowdsourced images, and computer vision techniques to provide social distancing decision support.

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IJCAI Conference 2021 Conference Paper

Communication-efficient and Scalable Decentralized Federated Edge Learning

Austine Zong Han Yapp
Hong Soo Nicholas Koh
Yan Ting Lai
Jiawen Kang
Xuandi Li
Jer Shyuan Ng
Hongchao Jiang
Wei Yang Bryan Lim

Federated Edge Learning (FEL) is a distributed Machine Learning (ML) framework for collaborative training on edge devices. FEL improves data privacy over traditional centralized ML model training by keeping data on the devices and only sending local model updates to a central coordinator for aggregation. However, challenges still remain in existing FEL architectures where there is high communication overhead between edge devices and the coordinator. In this paper, we present a working prototype of blockchain-empowered and communication-efficient FEL framework, which enhances the security and scalability towards large-scale implementation of FEL.

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IJCAI Conference 2021 Conference Paper

Predictive Analytics for COVID-19 Social Distancing

Harold Ze Chie Teng
Hongchao Jiang
Xuan Rong Zane Ho
Wei Yang Bryan Lim
Jer Shyuan Ng
Han Yu
Zehui Xiong
Dusit Niyato

The COVID-19 pandemic has disrupted the lives of millions across the globe. In Singapore, promoting safe distancing by managing crowds in public areas have been the cornerstone of containing the community spread of the virus. One of the most important solutions to maintain social distancing is to monitor the crowdedness of indoor and outdoor points of interest. Using Nanyang Technological University (NTU) as a testbed, we develop and deploy a platform that provides live and predicted crowd counts for key locations on campus to help users plan their trips in an informed manner, so as to mitigate the risk of community transmission.

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IS Journal 2020 Journal Article

A Sustainable Incentive Scheme for Federated Learning

Han Yu
Zelei Liu
Yang Liu
Tianjian Chen
Mingshu Cong
Xi Weng
Dusit Niyato
Qiang Yang

In federated learning (FL), a federation distributedly trains a collective machine learning model by leveraging privacy preserving technologies. However, FL participants need to incur some cost for contributing to the FL models. The training and commercialization of the models will take time. Thus, there will be delays before the federation could pay back the participants. This temporary mismatch between contributions and rewards has not been accounted for by existing payoff-sharing schemes. To address this limitation, we propose the FL incentivizer (FLI). It dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff received and the waiting time for receiving payoffs. Comparisons with five state-of-the-art payoff-sharing schemes show that FLI attracts high-quality data owners and achieves the highest expected revenue for a federation.

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