A Crowdsourcing Framework for On-Device Federated Learning (1911.01046v2)

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.

• The paper presents a novel incentive-based crowdsourcing framework that employs a two-stage Stackelberg game to optimize federated learning.
• It demonstrates up to 22% improvement in client rewards while maintaining global model accuracy by balancing communication and computation costs.
• The framework decouples global optimization into distributed subproblems, offering scalable and efficient on-device learning solutions.

Analysis of a Crowdsourcing Framework for On-Device Federated Learning

This paper addresses a critical challenge in the field of Federated Learning (FL), which pertains to improving the global model quality while maintaining communication efficiency among distributed devices. It proposes a novel crowdsourcing framework leveraging an incentive mechanism to optimize FL in a decentralized setting.

Federated Learning (FL) is an emerging paradigm allowing data to remain localized on devices while training a global model through decentralized computations. This technique aligns with privacy concerns and data minimization principles by aggregating updates instead of raw data. However, a significant challenge lies in effectively managing the communication overhead that arises during the parameter exchanges between clients and the central coordinating server. The authors tackle this issue by formulating a utility maximization problem within the context of a crowdsourced platform.

Core Framework Description

The proposed framework integrates an economic interaction model between the central server (MEC server) and the participating mobile clients, employing a two-stage Stackelberg game. The Stackelberg game naturally structures the problem in two hierarchical stages:

1. Client Strategy (Stage II): After receiving the reward rate from the server, each client independently maximizes its utility by selecting a local accuracy level that balances computation and communication costs. The clients’ utility is determined by the offered reward and the incurred costs from computing and communication efforts. The client’s local problem is addressed under the assumption of a linear valuation function decreasing with local accuracy.
2. Server Strategy (Stage I): The server determines the optimal reward rate to maximize its utility, defined in terms of the improvement achieved in the global model through the local solutions of the clients. This utility considers the trade-off between the reward costs and the enhanced quality of the global model clustering around an optimal client consensus accuracy level.

Strong Numerical Results

The authors report substantial efficacy in their proposed solution mechanism through simulation results, demonstrating up to a 22% gain in the reward offered to clients while maintaining desired accuracy levels in the global model. These results underscore the effectiveness of leveraging a utility-driven participatory framework to optimize the FL process in terms of communication efficiency and model quality.

Theoretical Implications

The theoretical implications of this work are considerable. By utilizing the duality in optimization, the authors adeptly decouple the global problem into distributed subproblems suitable for federated computation. This approach enables a direct evaluation of communication costs versus computation gains across participating clients, thereby optimizing resource allocation in practical FL deployments.

Practical Implications and Future Directions

From a practical standpoint, this research provides a scalable model for incentivizing client participation in FL settings, crucial for large-scale applications where device heterogeneity could otherwise hinder performance. Moreover, the admission control strategy offers a probabilistic model for estimating optimal participation to achieve desired global accuracy levels efficiently.

With future AI developments in mind, this framework anticipates the potential for on-device intelligence, where local processing is balanced against network-wide goals. Future research might explore adaptive pricing mechanisms beyond uniform rates, potentially leveraging real-time data or personalized incentives based on client-specific preferences or capabilities.

In conclusion, this paper advances the field by integrating economic principles into federated learning, offering a robust framework for optimizing communication efficiencies and model accuracies. It serves as a foundational work in aligning decentralized machine learning processes with market-driven participation models, thereby opening avenues for further exploration in federated ecosystems.