DeRelayL: Sustainable Decentralized Relay Learning

2026-04-30

AI Summary

This work addresses key limitations of existing collaborative learning approaches—such as federated learning—pertaining to user ownership, sustainability, and high resource barriers. To overcome these challenges, the authors propose a novel paradigm termed Decentralized Relay Learning (DeRelayL), which establishes a permissionless decentralized system enabling low-barrier participation through a relay-based model update mechanism. DeRelayL integrates a sustainable incentive scheme to ensure long-term viability. Theoretical analysis and simulation experiments demonstrate that DeRelayL significantly outperforms current methods in training efficacy, participant sustainability, and fairness in resource sharing, thereby transcending conventional constraints imposed by data privacy requirements and centralized aggregation architectures in collaborative learning.

Abstract

In the era of big data, large-scale machine learning models have revolutionized various fields, driving significant advancements. However, large-scale model training demands high financial and computational resources, which are only affordable by a few technological giants and well-funded institutions. In this case, common users like mobile users, the real creators of valuable data, are often excluded from fully benefiting due to the barriers, while the current methods for accessing large-scale models either limit user ownership or lack sustainability. This growing gap highlights the urgent need for a collaborative model training approach, allowing common users to train and share models. However, existing collaborative model training paradigms, especially federated learning (FL), primarily focus on data privacy and group-based model aggregation. To this end, this paper intends to address this issue by proposing a novel training paradigm named decentralized relay learning (DeRelayL), a sustainable learning system where permissionless participants can contribute to model training in a relay-like manner and share the model. In detail, this paper presents the architecture and workflow of DeRelayL, designs incentive mechanisms to ensure sustainability, and conducts theoretical analysis and numerical simulations to demonstrate its effectiveness.

Problem

• decentralized learning
• sustainable training
• collaborative model training
• federated learning
• model sharing

Innovation

• Decentralized Relay Learning
• Sustainable Machine Learning
• Permissionless Collaboration
• Incentive Mechanism
• Model Ownership