Ali H. Sayed

TL;DR: The exact diffusion method is applicable to locally balanced left-stochastic combination matrices which, compared to the conventional doubly stochastic matrix, are more general and able to endow the algorithm with faster convergence rates, more flexible step-size choices, and improved privacy-preserving properties.

TL;DR: Adaptive filters are at the core of many signal processing applications, ranging from acoustic noise supression to echo cancelation to array beamforming.

TL;DR: In this paper, a distributed optimization strategy with guaranteed exact convergence for a broad class of left-stochastic combination policies was developed, which is applicable to locally balanced combination matrices which are more general and able to endow the algorithm with faster convergence rates, more flexible step-size choices, and improved privacy-preserving properties.

TL;DR: The resulting adaptive networks are robust to node and link failures and present a substantial improvement over the non-cooperative case asserting that cooperation improves estimation performance.

TL;DR: An overview of recent work on distributed adaptive algorithms focuses mainly on incremental and diffusion strategies and comments on the mean-square-error performance of the incremental solution.