Existing approaches to federated learning suffer from a communication bottleneck as well as
convergence issues due to sparse client participation. In this paper we introduce a novel …

Large-scale distributed training of neural networks is often limited by network bandwidth,
wherein the communication time overwhelms the local computation time. Motivated by the …

We investigate the adversarial robustness of streaming algorithms. In this context, an
algorithm is considered robust if its performance guarantees hold even if the stream is …

We consider relative error low rank approximation of tensors with respect to the Frobenius
norm. Namely, given an order-q tensor A∊ ℝ∏ i= 1 q ni, output a rank-k tensor B for which …

In the adversarially robust streaming model, a stream of elements is presented to an
algorithm and is allowed to depend on the output of the algorithm at earlier times during the …

Online learning with expert advice is a fundamental problem of sequential prediction. In this
problem, the algorithm has access to a set of n “experts” who make predictions on each day …

Estimating frequencies of elements appearing in a data stream is a key task in large-scale
data analysis. Popular sketching approaches to this problem (eg, CountMin and …

The notion of L p sampling, and corresponding algorithms known as L p samplers, has
found a wide range of applications in the design of data stream algorithms and beyond. In …

We consider the classic Euclidean k-median and k-means objective on data streams, where
the goal is to provide a (1+ε)-approximation to the optimal k-median or k-means solution …

We develop a new algorithm for the turnstile heavy hitters problem in general turnstile
streams, the EXPANDERSKETCH, which finds the approximate top-k items in a universe of …