In the rapidly evolving landscape of Web3 and blockchain technologies, decentralized autonomous organizations (DAOs) have emerged as innovative structures that operate autonomously through blockchain and smart contracts, eliminating the need for centralized control. The federated learning (FL) process, akin to an information flow under structured transparency, involves local models (LMs) as inputs and the global model (GM) as the output for each global iteration. The lack of transparency and security in traditional FL systems can be attributed to the centralized validation of LMs and GM updates. In this paper, we propose DAO-FL, a smart contract-based framework that leverages the power of DAOs to address these FL challenges. DAO-FL introduces the concept of DAO Membership Tokens (DAOMTs) as a governance tool within a DAO. DAOMTs play a crucial role within the DAO, facilitating members’ enrollment and expulsion. Our framework incorporates a Validation-DAO for decentralized input verification (DIV) of the FL process, ensuring reliable and transparent validation of LMs. Additionally, DAO-FL employs a multi-signatures approach facilitated by an Orchestrator-DAO to achieve decentralized GM updates, and thus decentralized output verification (DOV) of the FL process. We present a comprehensive system architecture, detailed execution workflow, implementation specifications, and qualitative evaluation for DAO-FL. Evaluation under threat models highlights DAO-FL’s out-performance against traditional centralized-FL, effectively countering input and output attacks. DAO-FL excels in scenarios where DIV and DOV are crucial, offering enhanced transparency and trust. In conclusion, DAOFL provides a compelling solution for FL, reinforcing the integrity of the FL ecosystem through decentralized decision making and validation mechanisms.

This research introduces a reconfigurable intelligent surface (RIS)-assisted UAV service delivery system designed to enhance the network quality-of-experience (QoE) of remote users (RUs) by improving signal propagation and mitigating channel uncertainty. The RIS-enabled UAVs are deployed to ensure robust connectivity between RUs and the network infrastructure service provider (NISP). The objective is to maximize RU communication rates while satisfying quality of service (QoS) requirements. This optimization problem is formulated as a mixed-integer nonlinear programming (MILP) challenge, decomposed into channel allocation and RIS phase shift optimization sub-problems. These sub-problems are solved iteratively using matching game and whale optimization algorithms. Simulation results demonstrate that the proposed system significantly outperforms benchmark schemes in improving communication rates.