Flower
Flower is an open source federated learning framework designed to simplify the development and deployment of machine learning models across decentralized data sources. It enables training on data located on devices or servers without transferring the data itself, thereby enhancing privacy and reducing bandwidth usage. Flower supports a wide range of machine learning frameworks, including PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, and XGBoost, and is compatible with various platforms and cloud services like AWS, GCP, and Azure. It offers flexibility through customizable strategies and supports both horizontal and vertical federated learning scenarios. Flower's architecture allows for scalable experiments, with the capability to handle workloads involving tens of millions of clients. It also provides built-in support for privacy-preserving techniques like differential privacy and secure aggregation.
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PlatON
Combining blockchain and privacy-preserving computation technologies, PlatON is building a decentralized and collaborative AI network and global brain to drive the democratization of AI for safe artificial general intelligence. PlatON, initiated and driven by the LatticeX Foundation, is a next-generation Internet infrastructure protocol based on the fundamental properties of blockchain and supported by the privacy-preserving computation network. “Computing interoperability” is its core feature. By building a computing system assembled by verifiable computation, secure multi-party computation, zero-knowledge proof, homomorphic encryption and other cryptographic algorithms and blockchain technology, PlatON provides a public infrastructure in open source architecture for global artificial intelligence, distributed application developers, data providers and various organizations, communities and individuals with computing needs.
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Twine AI
Twine AI offers tailored speech, image, and video data collection and annotation services, including off‑the‑shelf and custom datasets, for training and fine‑tuning AI/ML models. It offers audio (voice recordings, transcription across 163+ languages and dialects), image and video (biometrics, object/scene detection, drone/satellite feeds), text, and synthetic data. Leveraging a vetted global crowd of 400,000–500,000 contributors, Twine ensures ethical, consent‑based collection and bias reduction with ISO 27001-level security and GDPR compliance. Projects are managed end‑to‑end through technical scoping, proofs of concept, and full delivery supported by dedicated project managers, version control, QA workflows, and secure payments across 190+ countries. Its service includes humans‑in‑the‑loop annotation, RLHF techniques, dataset versioning, audit trails, and full dataset management, enabling scalable, context‑rich training data for advanced computer vision.
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Findora
Deploy financial infrastructures with privacy-preserving transparency. Findora enables assets of any nature - dollar, bitcoin, equity, debt and derivatives. Its mission is to address the challenges necessary to support this broad class of assets and diverse financial use cases, providing confidentiality while retaining the transparency of other blockchains. Findora uses zero-knowledge proofs and secure multi-party computation to support many privacy-preserving features. Specialized zero-knowledge proofs allow Findora to be publicly audited, while data remains confidential. Findora features a high-throughput ledger design, and reduces storage requirements through cryptographic accumulators. Findora breaks open data silos for easy interoperability between the main- and side-ledgers. Findora provides you with the tools, documentation, and support to help you build your applications. Develop privacy-preserving applications on the Findora testnet today.
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