COLA: Decentralized Linear Learning
This addresses data ownership and privacy challenges in machine learning for decentralized settings, representing a novel method for a known bottleneck.
The paper tackles the problem of decentralized linear learning on user devices without a central coordinator, proposing COLA, which achieves communication efficiency, scalability, elasticity, and resilience with strong theoretical guarantees and superior practical performance.
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over many user devices, and the learning algorithm must run on-device, on an arbitrary communication network, without a central coordinator. We propose COLA, a new decentralized training algorithm with strong theoretical guarantees and superior practical performance. Our framework overcomes many limitations of existing methods, and achieves communication efficiency, scalability, elasticity as well as resilience to changes in data and participating devices.