Scalable Machine Learning Through Segmented Co-Operation In Distributed Systems

Decentralized machine learning has become a pivotal focus in recent years, marking a transformative era in the realm of artificial intelligence. In such decentralized frameworks, each participating node retains control over its local data, addressing concerns related to privacy and data ownership. As these models grow in complexity, the demand for scalable and efficient training methodologies intensifies. Learning frameworks can be compared by focusing on key aspects like communication efficiency, model accuracy, and defense against attacks. This work highlights the communication efficiency of these frameworks, emphasizing the trade-offs and advancements in Segmented Cooperative Machine Learning, a segment-based learning approach that demonstrates superior efficiency and robust defense mechanism. Federated Learning, a pioneering approach in decentralized learning, is juxtaposed with our proposed Segmented Cooperative Machine Learning framework, shedding light on their respective strengths and limitations. The introduction of an adversarial threat model for Segmented Cooperative Machine Learning serves as a stepping stone for future research in advanced defense strategies tailored to secure decentralized frameworks.