Decentralized Multi-Task Learning: Communication-Efficient and Provable

This paper addresses the challenge of decentralized multi-task representation learning, a crucial area for data-scarce environments. It proposes a novel algorithm with provable guarantees on accuracy, time, communication, and sample complexities. The key contribution is the communication complexity's independence from target accuracy, offering significant communication cost reduction. The paper's focus on decentralized methods, especially in comparison to centralized and federated approaches, is particularly relevant.

• •Proposes a new decentralized multi-task representation learning algorithm.
• •Provides provable accuracy guarantees.
• •Communication complexity is independent of target accuracy, reducing communication cost.
• •Demonstrates potential advantages over centralized and federated approaches in certain regimes.