Balancing Communication and Acceleration: Exact One-to-One Optimization for Distributed Multiagent Learning Systems.

Area of Science:

• Distributed Systems
• Machine Learning Optimization

Background:

• Distributed multiagent systems face a trade-off between communication efficiency and convergence speed.
• Existing accelerated methods often require multiple communications per iteration, hindering efficiency.

Purpose of the Study:

• To develop novel optimization-driven learning techniques for distributed systems.
• To achieve accelerated convergence with reduced communication overhead.

Main Methods:

• Proposed two new methods: Heavy-Ball Exact Fusion (HBEF) and Nesterov-Accelerated Exact Fusion (NAEF).
• Fused momentum mechanisms with bias correction for enhanced convergence.
• Introduced a dual acceleration method via momentum parameter coordination.

Main Results:

• Achieved enhanced convergence with a single communication operation per iteration.
• Demonstrated accelerated convergence compared to baseline and contemporary methods.
• Validated superiority in transient speed and steady-state accuracy across machine learning tasks.

Conclusions:

• The proposed methods, HBEF and NAEF, effectively balance communication efficiency and convergence acceleration.
• Achieved state-of-the-art performance at significantly reduced communication costs (half or one-third).
• Highlighted the critical role of momentum parameter coordination in distributed learning acceleration.