Business Plan Sections
Problem
Decentralized machine learning systems suffer from communication inefficiencies and straggler problems, leading to slow training times and scalability limitations, especially in resource-constrained environments. Existing federated learning methods often rely on a central server, creating a bottleneck.
Unique Value Proposition
Up to 71% reduction in training time compared to existing methods, without the need for a central server, improved robustness and scalability, compatible with non-IID data and large deep models, maintains high model accuracy under privacy constraints, adaptable to heterogeneous device environments.
Core Offerings
ComDML software framework for communication-efficient decentralized multi-agent machine learning.
Our Customers
Companies and research institutions involved in developing and deploying decentralized AI systems, particularly in resource-constrained or privacy-sensitive environments (e.g., mobile and IoT devices, edge computing, smart cities, healthcare, finance).