AutoFL: A Bayesian Game Approach for Autonomous Client Participation in Federated Edge Learning

Hu, Miao; Yang, Wenzhuo; Luo, Zhenxiao; Liu, Xuezheng; Zhou, Yipeng; Chen, Xu; Wu, Di

doi:10.1109/tmc.2022.3227014

Cited by 7 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An incomplete information game is comprised of multiple agents who takes actions which maximizes their utilities depending on actions of other agents and unknown states. Incomplete information games are used to model federated edge learning [1], electricity spot market [2], cyber defense in EV charging [3] and traffic flow in communication or transportation networks [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization Approach to Information Design in Linear-Quadratic-Gaussian Games

Sezer¹,

Eksin²

2023

Preprint

View full text Add to dashboard Cite

Information design in an incomplete information game includes a designer with the goal of influencing players' actions through signals generated from a designed probability distribution so that its objective function is optimized. We consider a setting in which the designer has partial knowledge on agents' utilities. We address the uncertainty about players' preferences by formulating a robust information design problem against worst case payoffs. If the players have quadratic payoffs that depend on the players' actions and an unknown payoff-relevant state, and signals on the state that follow a Gaussian distribution conditional on the state realization, then the information design problem under quadratic design objectives is a semidefinite program (SDP). Specifically, we consider ellipsoid perturbations over payoff coefficients in linear-quadratic-Gaussian (LQG) games. We show that this leads to a tractable robust SDP formulation. Numerical studies are carried out to identify the relation between the perturbation levels and the optimal information structures.

show abstract

Section: Introductionmentioning

confidence: 99%

Robust Optimization Approach to Information Design in Linear-Quadratic-Gaussian Games

Sezer¹,

Eksin²

2023

Preprint

View full text Add to dashboard Cite

show abstract

Privacy‐preserving model splitting and quality‐aware device association for federated edge learning

Dong

Shen

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

Federated edge learning (FEEL) provides a promising device‐edge collaborative learning paradigm, which enables edge devices to parallel participate in model co‐creation while preserving user privacy, opening countless opportunities to enable edge intelligence. With the growing demand for intelligent services, extensive FEEL deployment is inevitable. Nevertheless, existing FL schemes neglect two unique features (i.e., resource heterogeneity and data heterogeneity) in real‐world edge learning and thus may negatively affect the training efficiency and accuracy. Specifically, (1) heterogeneous and limited device resources cause massive laggards, which bring intolerable training delay; (2) heterogeneous data distribution causes device quality divergence, bringing severe training accuracy degradation. This article proposes a split‐based FEEL framework and an adaptive model splitting and quality‐aware device association scheme (MSDA) to tackle the aforementioned challenges. MSDA contains two levels: at the model splitting level, according to device capability and model structure, an adaptive splitting mechanism is proposed to provide a low‐latency and privacy‐preserving model splitting strategy for each device and guide subsequent device association. At the device association level, each device is simulated as a player with a quality weight in the potential game. Then a quality‐aware decentralized device association mechanism is designed to ensure that more high‐quality devices upload local updates before the deadline with the help of the edge server. Finally, experimental results demonstrate that MSDA yields significant improvements, achieving up to 3.1 training speedup and 39% accuracy improvement compared to state‐of‐the‐art methods.

show abstract

A Dynamic Adaptive and Resource-Allocated Selection Method Based on TOPSIS and VIKOR in Federated Learning

Li,

Shi,

Chen

et al. 2024

Neural Process Lett

View full text Add to dashboard Cite

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique that protects data privacy by learning models locally and not sharing datasets. However, due to limited computing resources on devices and highly heterogeneous data in practical situations, the training efficiency and resource utilization of federated learning is low. In order to resolve these challenges, we introduce a blockchain-assisted dynamic adaptive and personalized federated learning framework (TV-FedAvg) in the presence of restricted computing power resources and data heterogeneity. After each round of local training, we utilize an improved scoring model based on VIKOR and TOPSIS to comprehensively score the devices. The scores are then utilized to choose devices for participation in global aggregation and to carry out model aggregation through blockchain consensus. Furthermore, resources are reallocated for the next round to enhance resource efficiency, model fairness, and performance. Finally, we demonstrate through experimentation that TV-FedAvg outperforms other models such as pFedMe, FedAvg, Per-FedAvg, and TOPSIS in terms of both efficiency and performance.

show abstract

AutoFL: A Bayesian Game Approach for Autonomous Client Participation in Federated Edge Learning

Cited by 7 publications

References 42 publications

Robust Optimization Approach to Information Design in Linear-Quadratic-Gaussian Games

Robust Optimization Approach to Information Design in Linear-Quadratic-Gaussian Games

Privacy‐preserving model splitting and quality‐aware device association for federated edge learning

A Dynamic Adaptive and Resource-Allocated Selection Method Based on TOPSIS and VIKOR in Federated Learning

Contact Info

Product

Resources

About