A Crowdsourcing Framework for On-Device Federated Learning

By Shashi Raj Pandey, Nguyen H. Tran, Medhi Bennis, Yan Kyaw Tun, Aunas Manzoor, Choong Seon Hong. IEEE Transactions on Wireless Communications, 2019

296 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Science Applications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/92fdaf04924016f006f45fc145a4eb799dc731ec

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Citations in the corpus

Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “Given a crowdsourcing/crowdsensing system, the work in [96] focuses on design-”
A Survey of Incentive Mechanism Design for Federated Learning (2021, unknown): “Similar to the above work, Pandey et al. propose a novel crowdsourcing platform by constructing a communication-ef ﬁ cient cost model that considers the communication ef ﬁ - ciency during model parameters exchange [38].”
Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, unknown): “enabling intelligent edge-based smart healthcare applications [90], [91].”
Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020, unknown): “[51] propose a crowdsourcing framework to sup-”
Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach (2020, methodology): “In [13], the authors adopt the Stackelberg game to study the interaction between participating clients and an edge server.”
Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challenges (2021, background): “A number of possible research directions are also highlighted.”
Federated Machine Learning: Survey, Multi-Level Classification, Desirable Criteria and Future Directions in Communication and Networking Systems (2021, methodology): “of [179] propose an incentive mechanism that employs a twostage Stackelberg game model which aims to jointly maximize the utility of the edge server and clients that interact via a”
A review of applications in federated learning (2020, methodology): “The crowdsourcing framework adopted two-stage stackelberg model to acquire utility maximization among clients and server (Pandey et al., 2019).”