Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial step of FL, that of aggregating local models to produce the global one, shares many similarities with ...

Training deep learning (DL) models in the cloud has become a norm. With the emergence of serverless computing and its benefits of true pay-as-you-go pricing and scalability, systems researchers have recently started to provide support for serverless-...

In this paper, we formulate and study a new, but basic, distributed resource allocation problem arising in scaling blockchain performance. While distributed resource allocation is a well-studied problem in networking, the blockchain setting additionally ...

We consider a system of N ~servers inter-connected by some underlying graph topology~G N . Tasks with unit-mean exponential processing times arrive at the various servers as independent Poisson processes of rate lambda. Each incoming task is irrevocably ...

We consider the distributed statistical learning problem over decentralized systems that are prone to adversarial attacks. This setup arises in many practical applications, including Google's Federated Learning. Formally, we focus on a decentralized ...

One of the major issues with the integration of renewable energy sources into the power grid is the increased uncertainty and variability that they bring. If this uncertainty is not sufficiently addressed, it will limit the further penetration of ...

In recent years, enterprise applications faced an ever growing complexity of business processes as well as an increase in the number of interacting hardware and software components. The ability to efficiently manage their IT infrastructure up to the ...

We consider the problem of task assignment in a distributed system (such as a distributed Web server) in which task sizes are drawn from a heavy-tailed distribution. Many task assignment algorithms are based on the heuristic that balancing the load at ...

We consider an important tradeoff between processor and memory allocation in distributed parallel processing systems. To study this tradeoff, we formulate stochastic models of parallel program behavior, distributed parallel processing environments and ...

Queueing network models are effective tools for capacity planning of computer systems. The base of all performance oriented questions is the characterization of the computer system workload. At the capacity planning level the workload is described in ...

Our work deals with the analysis of the queueing delays of buffered multistage Banyan networks of multiprocessors. We provide tight upper bounds on the mean delays of the second stage and beyond, in the case of infinite buffers. Our results are ...