A Crowdsourcing Framework for On-Device Federated Learning

by   Shashi Raj Pandey, et al.

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


page 11

page 16

page 18

page 21

page 25

page 27

page 32

page 33


Incentivize to Build: A Crowdsourcing Framework for Federated Learning

Federated learning (FL) rests on the notion of training a global model i...

Incentive-boosted Federated Crowdsourcing

Crowdsourcing is a favorable computing paradigm for processing computer-...

Crowdsourcing-based Multi-Device Communication Cooperation for Mobile High-Quality Video Enhancement

The widespread use of mobile devices propels the development of new-fash...

RC-SSFL: Towards Robust and Communication-efficient Semi-supervised Federated Learning System

Federated Learning (FL) is an emerging decentralized artificial intellig...

FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training

Federated Learning (FL) enables collaborations among clients for train m...

FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels

Federated learning (FL) aims at training a global model on the server si...

Gradual Federated Learning with Simulated Annealing

Federated averaging (FedAvg) is a popular federated learning (FL) techni...

Please sign up or login with your details

Forgot password? Click here to reset