13 citations · 38 across the 16 of their papers we have counts for
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Distributed Stochastic Approximation Algorithms and Heavy-Tailed Age of Information
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Algorithms in multi-agent systems such as federated learning, mobile robotic swarming, and consensus control can be designed and analyzed as distributed stochastic approximation al…
Stability and Convergence of Distributed Stochastic Approximations with large Unbounded Stochastic Information Delays
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
We generalize the Borkar-Meyn stability Theorem (BMT) to distributed stochastic approximations (SAs) with information delays that possess an arbitrary moment bound. To model the de…
Distributed gradient-based optimization in the presence of dependent aperiodic communication
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence…
Practical sufficient conditions for convergence of distributed optimisation algorithms over communication networks with interference
Adrian Redder, Arunselvan Ramaswamy, Holger Karl
Information exchange over networks can be affected by various forms of delay. This causes challenges for using the network by a multi-agent system to solve a distributed optimisati…
DSPG: Decentralized Simultaneous Perturbations Gradient Descent Scheme
Arunselvan Ramaswamy
Distributed descent-based methods are an essential toolset to solving optimization problems in multi-agent system scenarios. Here the agents seek to optimize a global objective fun…
Asynchronous stochastic approximations with asymptotically biased errors and deep multi-agent learning
Arunselvan Ramaswamy, Shalabh Bhatnagar, Daniel E. Quevedo
Asynchronous stochastic approximations (SAs) are an important class of model-free algorithms, tools and techniques that are popular in multi-agent and distributed control scenarios…