FedML Powers Private, Scalable, and Collaborative AI in Healthcare Solutions for Konica Minolta
LOS ANGELES–(BUSINESS WIRE)–FedML, a decentralized and collaborative machine learning platform that enables training, deployment, and continual improvement of AI models anywhere, announced today that it would partner with Konica Minolta, a Japanese multinational technology company manufacturing business, industrial and medical imaging products, including copiers, laser printers, multi-functional peripherals (MFPs), digital print systems for the production printing, textile printers, medical and graphic imaging products, such as X-ray image processing systems, ultrasound systems, color proofing systems, and X-ray film; photometers, 3-D digitizers, and other sensing products.
“Large machine learning (ML) models are breaking new ground every day, achieving unprecedented performance in various application domains, such as computer vision, speech and language processing, data mining, healthcare, and life sciences,” said Salman Avestimehr, CEO and Co-Founder of FedML. “However, the performance of such models heavily depends on the size and diversity of the datasets, as well as the quality of their annotations. This creates a major pain point in the development and utilization of state-of-the-art ML models in the healthcare domain, as medical data obeys strict regulatory laws and privacy restrictions and is very costly to annotate. This leaves most institutions with their own datasets, limiting AI from reaching its full potential in health tech.”
Fortunately, FedML allows AI technologies to use these siloed 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 privacy, reduces development costs, and furthermore empowers data owners to monetize their data and compute resources.
“It has become clear that machine learning and AI can greatly impact healthcare by improving diagnostics, patient safety, and treatment planning. According to the recent market research data, the projected market size of AI-based global healthcare solutions is expected to reach $200B+ by 2030. However, healthcare data, which is essentially the fuel for machine learning, is very much siloed and heterogeneous due to privacy rules (e.g., HIPAA, GDPR, CCPA, etc), data regulations, disconnected IT infrastructure of healthcare systems, and diverse procedures for data collection and annotation at different healthcare institutions. FedML provides the much needed ML platform for developing AI applications in the healthcare ecosystem with isolated and heterogeneous data silos,” said Dr. Jun Amano, Open Innovation Lead at Konica Minolta Business Solutions USA.
In this partnership, FedML will empower Konica Minolta to launch collaborative and privacy-preserving training, serving, and monitoring of ML models for Medical Imaging and Radiomics across decentralized data silos (e.g., different hospitals) with diverse annotation quality. The partnership will also help strengthening FedML’s platform for further adoptions in the healthcare domain.
FedML (https://fedml.ai) provides an open-source community and an enterprise platform for decentralized and collaborative AI anywhere (at the edge or over the cloud) at any scale. More specifically, FedML provides a MLOps ecosystem that enables training, deployment, monitoring, and continual improvement of machine learning models, while empowering collaboration on combined data, models, and computing resources in a privacy-preserving manner.
FedML’s platform is backed by a large open-source community (top ranking GitHub library on federated learning), and its enterprise platform is currently used by 1600+ developers and 10+ large enterprise customers worldwide. FedML’s AI application ecosystem also supports a large range of AI+ verticals, including but not limited to Health and Life Sciences, AIoT + Computer Vision, Generative AI, BioTech, Smart Home and Smart City, Retail Solution, Logistics, and FinTech.