TensorOpera FedML Platform

FedML (https://FedML) belongs to TensorOpera AI, an independent C-corp company in the US. At the same time, it contributes part of ChainOpera AI Foundation.

TensorOpera® FedML is part of TensorOpera AI cloud. It is a machine learning platform that enables zero-code, lightweight, cross-platform, and provably secure federated learning and analytics. It enables machine learning from decentralized data at various users/silos/edge nodes without requiring data centralization to the cloud, thus providing maximum privacy and efficiency. It consists of a lightweight and cross-platform Edge AI SDK that is deployable over edge GPUs, smartphones, and IoT devices. Furthermore, it also provides a user-friendly MLOps platform to simplify decentralized machine learning and real-world deployment. FedML supports vertical solutions across a broad range of industries (healthcare, finance, insurance, smart cities, IoT, etc.) and applications (computer vision, natural language processing, data mining, and time-series forecasting). Its core technology is backed by many years of cutting-edge research by its co-founders.

TensorOpera®Federate builds simple and versatile APIs for machine learning running anywhere and at any scale. In other words, FedML supports both federated learning for data silos and distributed training for acceleration with MLOps and Open Source support, covering cutting-edge academia research and industrial grade use cases.

• TensorOpera®Federate Simulation - Simulating federated learning in the real world: (1) simulate FL using a single process (2) MPI-based FL Simulator (3) NCCL-based FL Simulator (fastest)

• TensorOpera®Federate Cross-silo - Cross-silo Federated Learning for cross-organization/account training, including Python-based edge SDK.

• TensorOpera®Federate Cross-device - Cross-device Federated Learning for Smartphones and IoTs, including edge SDK for Android/iOS and embedded Linux.

• TensorOpera AI - Federate: TensorOpera FedML's machine learning operation pipeline for AI running anywhere at any scale.