A Crowdsourcing Framework for On-Device Federated Learning

Abstract

Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentive-based interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22% gain in the offered reward.

arXiv commentAccepted in IEEE Transactions on Wireless Communications

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• Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence
• A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective
• Towards Fairness-Aware Federated Learning
• Edge Intelligence: Architectures, Challenges, and Applications
• Edge-assisted Democratized Learning Towards Federated Analytics
• A Contribution-based Device Selection Scheme in Federated Learning
• Adaptive Federated Learning and Digital Twin for Industrial Internet of Things
• Strategic Coalition for Data Pricing in IoT Data Markets
• Semi-Private Computation of Data Similarity with Applications to Data Valuation and Pricing
• ADI: Adversarial Dominating Inputs in Vertical Federated Learning Systems
• Estimation of Individual Device Contributions for Incentivizing Federated Learning
• An Exploratory Analysis on Users' Contributions in Federated Learning
• Faithful Edge Federated Learning: Scalability and Privacy
• Privacy-Aware Data Acquisition under Data Similarity in Regression Markets
• Trading Data For Learning: Incentive Mechanism For On-Device Federated Learning
• Utility Fairness for the Differentially Private Federated Learning
• Nothing Wasted: Full Contribution Enforcement in Federated Edge Learning
• On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator
• A Symmetric Loss Perspective of Reliable Machine Learning
• Energy-aware Resource Management for Federated Learning in Multi-access Edge Computing Systems
• A Coopetitive-Compatible Data Generation Framework for Cross-silo Federated Learning
• DeepSeek-Inspired Exploration of RL-based LLMs and Synergy with Wireless Networks: A Survey
• Low Complexity Recruitment for Collaborative Mobile Crowdsourcing Using Graph Neural Networks