10 citations · 29 across the 43 of their papers we have counts for
15 papers · 1 filter
Using Diffusion Models as Generative Replay in Continual Federated Learning -- What will Happen?
Yongsheng Mei, Liangqi Yuan, Dong-Jun Han +3
Federated learning (FL) has become a cornerstone in decentralized learning, where, in many scenarios, the incoming data distribution will change dynamically over time, introducing…
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis
Mohammadreza Kouchaki, Minglong Zhang, Aly S. Abdalla +3
In the rapidly evolving landscape of 5G technology, safeguarding Radio Frequency (RF) environments against sophisticated intrusions is paramount, especially in dynamic spectrum acc…
Communication-Efficient Federated Learning under Dynamic Device Arrival and Departure: Convergence Analysis and Algorithm Design
Zhan-Lun Chang, Dong-Jun Han, Seyyedali Hosseinalipour +2
Most federated learning (FL) approaches assume a fixed device set. However, real-world scenarios often involve devices dynamically joining or leaving the system, driven by, e.g., u…
Bridging SFT and DPO for Diffusion Model Alignment with Self-Sampling Preference Optimization
Daoan Zhang, Guangchen Lan, Dong-Jun Han +8
Existing post-training techniques are broadly categorized into supervised fine-tuning (SFT) and reinforcement learning (RL) methods; the former is stable during training but suffer…
Hierarchical Federated Learning with Multi-Timescale Gradient Correction
Wenzhi Fang, Dong-Jun Han, Evan Chen +2
While traditional federated learning (FL) typically focuses on a star topology where clients are directly connected to a central server, real-world distributed systems often exhibi…
A Hierarchical Gradient Tracking Algorithm for Mitigating Subnet-Drift in Fog Learning Networks
Evan Chen, Shiqiang Wang, Christopher G. Brinton
Federated learning (FL) encounters scalability challenges when implemented over fog networks that do not follow FL's conventional star topology architecture. Semi-decentralized FL…
Unlocking the Potential of Model Calibration in Federated Learning
Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour +1
Over the past several years, various federated learning (FL) methodologies have been developed to improve model accuracy, a primary performance metric in machine learning. However,…
Orchestrating Federated Learning in Space-Air-Ground Integrated Networks: Adaptive Data Offloading and Seamless Handover
Dong-Jun Han, Wenzhi Fang, Seyyedali Hosseinalipour +2
Devices located in remote regions often lack coverage from well-developed terrestrial communication infrastructure. This not only prevents them from experiencing high quality commu…
Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis
Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi +2
To improve the efficiency of reinforcement learning (RL), we propose a novel asynchronous federated reinforcement learning (FedRL) framework termed AFedPG, which constructs a globa…
Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees
Shahryar Zehtabi, Dong-Jun Han, Rohit Parasnis +2
Decentralized federated learning (DFL) captures FL settings where both (i) model updates and (ii) model aggregations are exclusively carried out by the clients without a central se…
Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks
Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour +1
A few recent studies have demonstrated that leveraging centrally pre-trained models can offer advantageous initializations for federated learning (FL). However, existing pre-traini…
Taming Subnet-Drift in D2D-Enabled Fog Learning: A Hierarchical Gradient Tracking Approach
Evan Chen, Shiqiang Wang, Christopher G. Brinton
Federated learning (FL) encounters scalability challenges when implemented over fog networks. Semi-decentralized FL (SD-FL) proposes a solution that divides model cooperation into…
Communication-Efficient Multimodal Federated Learning: Joint Modality and Client Selection
Liangqi Yuan, Dong-Jun Han, Su Wang +2
Multimodal federated learning (MFL) aims to enrich model training in FL settings where clients are collecting measurements across multiple modalities. However, key challenges to MF…
Differentially-Private Multi-Tier Federated Learning
Evan Chen, Frank Po-Chen Lin, Dong-Jun Han +1
While federated learning (FL) eliminates the transmission of raw data over a network, it is still vulnerable to privacy breaches from the communicated model parameters. In this wor…
Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
Yun-Wei Chu, Dong-Jun Han, Christopher G. Brinton
Federated learning (FL) is a promising distributed machine learning paradigm that enables multiple clients to collaboratively train a global model. In this paper, we focus on a pra…