arXiv:2302.09832v4 Announce Type: replace Abstract: In distributed optimization and federated learning, slow and costly communication between parallel devices and the central server constitutes the primary bottleneck. To alleviate this burden, two strategies have emerged: 1) local training (LT), which reduces communication frequency by performing multiple local computations between rounds, and 2) compression (CC), which consists of transmitting lower-dimensional, compact representations. Recent theoretical advances have successfully combined LT and CC to achieve doubly-accelerated communicatio
This research builds on recent theoretical advances in distributed optimization, a critical area for large-scale AI deployment, addressing key bottlenecks in federated learning and distributed computing.
Improved efficiency in distributed optimization directly impacts the scalability and cost-effectiveness of training and deploying complex AI models, particularly in scenarios with communication constraints.
The ability to combine local training and communication compression with 'doubly-accelerated' methods will lead to faster and more resource-efficient distributed AI systems, impacting training times and energy consumption.
- · AI model developers
- · Cloud computing providers
- · Edge AI developers
- · Researchers in distributed systems
- · Inefficient distributed algorithms
- · Systems with high communication overheads
Faster and cheaper training of large AI models, especially for federated learning applications.
Accelerated deployment of AI at the edge and in privacy-sensitive environments due to reduced communication needs.
Potentially democratized access to advanced AI capabilities by lowering computational and communication barriers.
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Read at arXiv cs.LG