Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization

We study distributed optimization problems over a network when the communication between the nodes is constrained, and therefore, information that is exchanged between the nodes must be quantized. Recent advances using the distributed gradient algorithm with a quantization scheme at a fixed resoluti...

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Veröffentlicht in:IEEE transactions on automatic control Jg. 66; H. 5; S. 2191 - 2205
Hauptverfasser: Doan, Thinh T., Maguluri, Siva Theja, Romberg, Justin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Bandwidth
Communication
Convergence
Convex functions
distributed consensus
Distributed optimization
distributed stochastic approximation
Linear programming
Measurement
Network topologies
Network topology
Nodes
Optimization
Quantization (signal)
quantized communication
ISSN:0018-9286, 1558-2523
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Zusammenfassung:We study distributed optimization problems over a network when the communication between the nodes is constrained, and therefore, information that is exchanged between the nodes must be quantized. Recent advances using the distributed gradient algorithm with a quantization scheme at a fixed resolution have established convergence, but at rates significantly slower than when the communications are unquantized. In this article, we introduce a novel quantization method, which we refer to as adaptive quantization, that allows us to match the convergence rates under perfect communications. Our approach adjusts the quantization scheme used by each node as the algorithm progresses: as we approach the solution, we become more certain about where the state variables are localized and adapt the quantizer codebook accordingly. We bound the convergence rates of the proposed method as a function of the communication bandwidth, the underlying network topology, and structural properties of the constituent objective functions. In particular, we show that if the objective functions are convex or strongly convex, then using adaptive quantization does not affect the rate of convergence of the distributed subgradient methods when the communications are quantized, except for a constant that depends on the resolution of the quantizer. To the best of our knowledge, the rates achieved in this article are better than any existing work in the literature for distributed gradient methods under finite communication bandwidths. We also provide numerical simulations that compare convergence properties of the distributed gradient methods with and without quantization for solving distributed regression problems for both quadratic and absolute loss functions.
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ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2020.3014095