arXiv:2607.01665v1 Announce Type: new Abstract: Decentralized online convex optimization (D-OCO) is a popular framework for distributed applications with streaming data. To tackle the communication bottleneck, previous studies have investigated D-OCO with compressed communication and proposed several algorithms that are variants of online gradient descent (OGD). However, for D-OCO with exact communication, the best existing algorithms are variants of follow-the-regularized-leader (FTRL). In this paper, for the first time, we propose two FTRL-type algorithms for D-OCO with compressed communicat
The continuous growth of distributed applications with streaming data drives the need for more efficient optimization techniques, especially regarding communication bottlenecks.
Improving decentralized online convex optimization with compressed communication can lead to more scalable and robust AI systems operating in distributed environments.
New FTRL-type algorithms provide a novel approach to D-OCO with compressed communication, which was previously dominated by OGD variants, potentially improving performance and efficiency.
- · Distributed AI systems
- · Edge computing applications
- · Researchers in optimization
- · Less efficient communication protocols
- · High-latency distributed systems
More efficient training and operation of large-scale decentralized machine learning models.
Reduced computational and communication overhead for AI applications across diverse network conditions.
Acceleration of truly distributed and federated AI, reducing reliance on centralized data processing and storage.
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