  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.

Machine Learning;Computer Science and Game Theory;Networking and Internet Architecture;Machine Learning