Our research explores the intersection of distributed computing, mathematical optimization, and artificial intelligence to build scalable, efficient, and reliable edge learning systems.
We design algorithms that integrate distributed architectures with optimization principles to improve the efficiency, fairness, and interpretability of modern AI-edge systems, with a focus on large language models, federated and edge learning, and real-time collaborative inference.
Highlighted 2025 Distributionally Robust Wireless Semantic Communication with Large AI Models
Long Tan Le, Senura Hansaja Wanasekara, Zerun Niu, and 7 more authors
2025
A distributionally robust approach for wireless semantic communication with large AI models that addresses uncertainty in wireless channels and semantic information transmission.
GoodSpeed: Optimizing Fair Goodput with Adaptive Speculative Decoding in Distributed Edge Inference
Phuong Tran, Tzu-Hao Liu, Long Tan Le, and 6 more authors
2025
Accepted at INFOCOM 2026
A distributed speculative decoding framework for multi-server edge inference that optimizes goodput while ensuring proportional fairness.
All Publications
2025 Distributionally Robust Wireless Semantic Communication with Large AI Models
Long Tan Le, Senura Hansaja Wanasekara, Zerun Niu, and 7 more authors
2025
A distributionally robust approach for wireless semantic communication with large AI models that addresses uncertainty in wireless channels and semantic information transmission.
GoodSpeed: Optimizing Fair Goodput with Adaptive Speculative Decoding in Distributed Edge Inference
Phuong Tran, Tzu-Hao Liu, Long Tan Le, and 6 more authors
2025
Accepted at INFOCOM 2026
A distributed speculative decoding framework for multi-server edge inference that optimizes goodput while ensuring proportional fairness.
2024 Distributionally Robust Federated Learning for Mobile Edge Networks
Long Tan Le, Tung-Anh Nguyen, Tuan-Dung Nguyen, and 5 more authors
Mobile Networks and Applications , 2024
A distributionally robust federated learning framework for mobile edge networks under data heterogeneity and distribution shifts.
Federated PCA on Grassmann Manifold for IoT Anomaly Detection
Tung-Anh Nguyen, Long Tan Le, Tuan Dung Nguyen, and 4 more authors
2024
A federated learning framework for large-scale multivariate time-series anomaly detection.
Federated Deep Equilibrium Learning: Harnessing Compact Global Representations to Enhance Personalization
Long Tan Le, Tuan Dung Nguyen, Tung-Anh Nguyen, and 4 more authors
2024
A federated deep equilibrium learning approach for personalization through compact global representations.
iREPO: Implicit Reward Pairwise Difference based Empirical Preference Optimization
Long Tan Le, Han Shu, Tung-Anh Nguyen, and 2 more authors
2024
An empirical preference optimization framework for aligning large language models with human expectations.
2023 A New Look and Convergence Rate of Federated Multitask Learning With Laplacian Regularization
Canh T. Dinh, Tung T. Vu, Nguyen H. Tran , and 2 more authors
IEEE Transactions on Neural Networks and Learning Systems , 2023
A federated multitask learning framework using Laplacian regularization with FedU and dFedU algorithms.
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks
Tung-Anh Nguyen, Jiayu He, Long Tan Le, and 2 more authors
In IEEE INFOCOM , 2023
A federated principal component analysis framework on Grassmann manifold for IoT anomaly detection.
2022 On the Generalization of Wasserstein Robust Federated Learning
Tung-Anh Nguyen, Tuan Dung Nguyen, Long Tan Le, and 2 more authors
2022
A Wasserstein distributionally robust optimization approach for federated learning under non-IID data and distribution shifts.
2020 Personalized Federated Learning with Moreau Envelopes
Canh T. Dinh, Nguyen H. Tran , and Tuan Dung Nguyen
2020
A personalized federated learning algorithm using Moreau envelopes for statistical heterogeneity in federated learning.
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
Canh T. Dinh, Nguyen H. Tran , Minh N. H. Nguyen, and 4 more authors
IEEE/ACM Transactions on Networking , 2020
A convergence analysis and resource allocation study for federated learning over wireless networks.