This paper introduces B4B.app, a Web3 influencer marketing protocol that leverages the Internet Computer (ICP) to solve persistent industry challenges such as fragmented data, unreliable ratings, and payment disputes. B4B.app delivers a trustless, cross-chain infrastructure combining influencer discovery, campaign management, and secure escrow payments in one seamless platform. At its core, B4B-ICP acts as a reputation and settlement layer, aggregating performance data from multiple social platforms and ensuring transparent, on-chain record keeping. This paper presents the platform’s architecture, user workflow, and implementation stack, along with key milestones and planned enhancements, including EVM interoperability and AI-driven performance prediction.

The field of Artificial Intelligence (AI) is rapidly evolving, creating a demand for sophisticated models that rely on substantial data and computational resources for training. However, the high costs associated with training these models have limited accessibility, leading to concerns about transparency, biases, and hidden agendas within AI systems. As AI becomes more integrated into governmental services and the pursuit of Artificial General Intelligence (AGI) advances, the necessity for transparent and reliable AI models becomes increasingly critical. Decentralized Federated Learning (DFL) offers decentralized approaches to model training while safeguarding data privacy and ensuring resilience against adversarial participants. Nonetheless, the guarantees provided are not absolute, and even open-weight AI models do not qualify as truly open source. This paper suggests using blockchain technology, smart contracts, and publicly verifiable secret sharing in DFL environments to bolster trust, cooperation, and transparency in model training processes. Our numerical experiments illustrate that the overhead required to offer robust assurances to all peers regarding the correctness of the training process is relatively small. By incorporating these tools, participants can trust that trained models adhere to specified procedures, addressing accountability issues within AI systems and promoting the development of more ethical and dependable applications of AI.

In the demo, we present B4B.World–an influencer marketing platform with reputation-based rewards. The platform allows influencers making payable advertising campaigns and earning rewards proportionally to the speed, quality, and quantity of ads. To get access to the active community of influencers, brands buy a monthly subscription and make campaigns by crypto or project-based NFTs. B4B. World prototype runs on Aurora EVM of Near blockchain testnet since November 2022. By February 2023, over 880 volunteers posted more than 4600 Twitter adds for 12 campaigns.

This demo addresses the challenges of creating influencer marketing campaigns, including difficulties in measuring campaign performance and payment risks. Blockchain technology can provide a solution by offering reliable influencer profile generation, transparent reputation models, and template escrow payments. However, communication and processing of payments across ecosystems remain a challenge for Web3 projects. The paper presents B4B.World, an influencer marketing platform that utilizes cross-chain smart contract calls and supports payments in multiple blockchains. The platform offers reputation-based rewards for influencers and allows advertisers to make campaigns using crypto assets or project-based NFTs. B4B.World is the winner both The Illuminate/22 Hack by Moonbeam and BNB Chain Hackathon 23 in Georgia and utilizes the Axelar platform for cross-chain interaction.