London, UK, December 19th, 2024, Chainwire
Led by DCG, the round underscores FLock’s momentum in reshaping AI through decentralized and private model training.
FLock.io, a leading innovator in decentralized AI model development and training, today announced the successful closure of a $3 million funding round, bringing its total raised to $11 million. The round was led by DCG, with participation from Faction, Animoca Brands, Fenbushi Capital, OKJ, GnosisVC, Bas1s Ventures, A41, and GSR. This milestone underscores FLock’s mission to democratize AI model development and will support the company’s next phase of growth as it continues to expand its innovative collaborative decentralized AI training platform.
The funding is the latest in a series of recent advancements for FLock, including its grant from the Ethereum Foundation to research incentive mechanisms for blockchain-based AI training, which has positioned the company at the forefront of AI democratization. The latest funding will fuel the scaling of FLock’s federated AI training platform, which combines blockchain technology with federated learning to enable secure, collaborative, and private AI model development.
As FLock advances, the $3 million funding will empower the company to scale its platform, prepare for mainnet launch, and deepen its engagement with private AI contributors worldwide. Currently, over 30 models are running on FLock from both Web2 and Web3 use cases, with over 1,500 training and validation nodes joining from communities and institutions like Numerai, Kaggle, Foundry, and Tensorplex, contributing to over 19,000 models created.
About FLock.io
FLock.io is a community-driven platform facilitating the creation of on-chain, decentralized AI models. By integrating federated learning and blockchain technology, FLock ensures equitable incentives for data contributors and fosters open collaboration. It also addresses the increasing need for advanced, bespoke AI models and reduces the risk of data breaches by providing secure model training without exposing source data.
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