An Incentive Auction for Heterogeneous Client Selection in Federated Learning

Federated Learning (FL) is a new distributed machine learning (ML) approach which enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models using local data without compromising user privacy. Although FL represents a promising computing paradigm, such training process can not be fully realized without an appropriate economic mechanism that incentivizes the participation of heterogeneous clients. This work targets social cost minimization, and studies the incentive mechanism design in FL through a procurement auction. Different from existing literature, we consider a practical scenario of FL where clients are selected and scheduled at different global iterations to guarantee the completion of the FL job, and capture the distinct feature of FL that the number of global iterations is determined by the local accuracy of all participants to balance between computation and communication. Our auction framework
$A_{FL}$
first decomposes the social cost minimization problem into a series of winner determination problems (WDPs) based on the number of global iterations. To solve each WDP,
$A_{FL}$
invokes a greedy algorithm to determine the winners, and a payment algorithm for computing remuneration to winners. Finally,
$A_{FL}$
returns the best solution among all WDPs. We carried out theoretical analysis to prove that
$A_{FL}$
is truthful, individual rational, computationally efficient, and achieves a near-optimal social cost. We further extend our model to consider multiple FL jobs with corresponding budgets and propose another efficient algorithm
$A_{FL-M}$
to solve the extended problem. We conduct large-scale simulations based on the real-world data and testbed experiments by adopting FL frameworks FAVOR and CoCoA. Simulation and experiment results show that both
$A_{FL}$
and
$A_{FL-M}$
can reduce the social cost by up to 55% compared with state-of-the-art algorithms.