Wednesday 09 April 2025
A new approach to incentivizing participation in federated learning, a type of decentralized machine learning, has been proposed by researchers. The method, called Right Reward Right Time (R3T), aims to encourage clients to contribute high-quality data and models during critical learning periods.
Federated learning allows multiple devices or organizations to collaboratively train an artificial intelligence model without sharing their private data. However, the process is often hindered by low-quality contributions from clients, which can negatively impact the overall performance of the global model. Critical learning periods (CLPs) are early stages during which these issues can have a lasting impact.
R3T addresses this challenge by providing the right reward at the right time. The cloud server, which manages the federated learning process, uses a utility function to capture the trade-off between achieved model performance and payments allocated for clients’ contributions. This approach takes into account factors such as the client’s time, system capabilities, efforts, joining time, and rewards.
The researchers have developed an optimal contract for the cloud server that incentivizes early participation and efforts while maximizing its own utility. They have also proposed a mechanism to detect CLPs and adjust the incentives accordingly.
Simulations and proof-of-concept studies have shown that R3T increases cloud utility and is more economically effective than benchmarked incentive mechanisms. The approach has been tested on several datasets, including Fashion-MNIST, and has achieved competitive test accuracies while reducing the total number of clients and convergence time.
The implications of R3T are significant, particularly in scenarios where data sharing is restricted or expensive. By providing a more efficient and effective way to incentivize participation, R3T could accelerate the adoption of federated learning in various industries, such as healthcare and finance.
The researchers plan to investigate the impact of free-riders and malicious attacks on the efficiency of R3T, as well as evaluate its performance on different blockchain platforms. Future work will also focus on developing more sophisticated utility functions that can adapt to changing network conditions.
In summary, Right Reward Right Time offers a promising solution for improving the efficiency and effectiveness of federated learning by providing incentives that are tailored to critical learning periods. As researchers continue to refine this approach, we may see widespread adoption in industries where data sharing is essential but challenging.
Cite this article: “Blockchain-Powered Federated Learning: A Novel Approach to Incentivize Client Participation and Enhance Model Performance”, The Science Archive, 2025.
Federated Learning, Decentralized Machine Learning, Incentivizing Participation, Right Reward Right Time, Critical Learning Periods, Cloud Server, Utility Function, Optimal Contract, Blockchain Platforms, Free-Riders, Malicious Attacks.
