FedML Announces Partnership With Theta Network To Empower Collaborative Machine Learning For Generative AI and Ad Recommendation

FedML, a Collaborative/Federated Machine Learning and Edge AI Platform that empowers communities to build and connect AI applications anywhere at any scale, announced today that it will partner with Theta Network to enable collaborative machine learning for Generative AI and content recommendation and advertisement, powered by the Theta edge network.

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Federated Learning is a solution to the data-sharing concern that the public and enterprises have (due to privacy, security, regulations, etc.) regarding the machine learning-powered technologies that modern society increasingly relies on. These technologies depend on huge, centralized data sets of sometimes sensitive/private information to make decisions on, say, private communication of the users for developing large language models for chatGPT-like chatbots or private browsing history and behaviors of the users for developing AI models for content recommendation and advertisement. While the community would enjoy the benefits of AI in these applications, they still want to keep their private information confidential.

Fortunately, FedML allows AI technologies to use these personal datasets without centralizing or transferring the data. It enables machine learning from decentralized data at various nodes without concentrating any data in the cloud (i.e., “learning without sharing”), which provides maximum user privacy, reduces development costs, and empowers data owners to monetize their data and compute resources.

“FedML’s mission is to connect and empower silos in the AI world (e.g., data owners and developers) to collaborate with each other” said Salman Avestimehr, CEO and Co-Founder of FedML. “At a high level, this is aligned with the overarching goal of web3 and blockchain systems, which is to connect and empower decentralized agents. This exciting partnership combines these two powerful visions to enable collaborative AI in web3. Ad recommendation and generative AI are just two immediate applications, where there is an immediate need for people to contribute their private data into the training of the machine learning models that they all benefit from.”

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Theta’s Edge Network, operated by 10s of thousands of decentralized nodes worldwide, makes up the perfect base for the types of distributed computing needed for collaborative machine learning and AI use cases. By leveraging the excess computational power, storage, and bandwidth of these Edge Nodes, FedML can enable groundbreaking advances in machine learning and AI via large-scale collaboration on combined data, models, and computing resources in a way that is inherently privacy-preserving and continually improving.

“Our collaboration with FedML unlocks a massive use case for Edge Nodes by enabling AI and ML development at a distributed, global level” said Mitch Liu, CEO and Co-Founder of Theta Labs. “As distributed compute usage continues to grow at an exponential rate, Theta Network’s Edge Nodes will grow alongside it to provide seamless support for these cutting edge applications in collaborative AI.”

This partnership particularly focuses on empowering two exciting use cases in web3: generative AI and content recommendation. In the first use-case, FedML enables collaborative training of large-scale generative AI models (e.g., chatGPT-like chatbots, Stable Diffusion-like text-to-image model) on the datasets of participating users in a privacy-preserving manner. In the second use-case, FedML enables Theta TV community members to contribute their personal preferences and compute resources to train and deploy AI models for personalized content recommendation and advertisement to them while being rewarded for their contributions (i.e., enabling AI by the community for the community). To deliver this Web3 x AI platform to the community, the two companies build an interdisciplinary team with experts in machine learning, blockchain, computer vision, natural language processing, security (zero-knowledge proof), and privacy.

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