Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Basat, Ran Ben"'
Gradient aggregation has long been identified as a major bottleneck in today's large-scale distributed machine learning training systems. One promising solution to mitigate such bottlenecks is gradient compression, directly reducing communicated grad
Externí odkaz:
http://arxiv.org/abs/2407.01378
Autor:
Li, Minghao, Basat, Ran Ben, Vargaftik, Shay, Lao, ChonLam, Xu, Kevin, Mitzenmacher, Michael, Yu, Minlan
Deep neural networks (DNNs) are the de facto standard for essential use cases, such as image classification, computer vision, and natural language processing. As DNNs and datasets get larger, they require distributed training on increasingly larger c
Externí odkaz:
http://arxiv.org/abs/2302.08545
Today's large-scale services (e.g., video streaming platforms, data centers, sensor grids) need diverse real-time summary statistics across multiple subpopulations of multidimensional datasets. However, state-of-the-art frameworks do not offer genera
Externí odkaz:
http://arxiv.org/abs/2208.04927
Autor:
Basat, Ran Ben, Vargaftik, Shay, Portnoy, Amit, Einziger, Gil, Ben-Itzhak, Yaniv, Mitzenmacher, Michael
Distributed Mean Estimation (DME), in which $n$ clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-efficient federated learning. In this paper, we improve on previous DME t
Externí odkaz:
http://arxiv.org/abs/2205.13341
Autor:
Langlet, Jonatan, Basat, Ran Ben, Oliaro, Gabriele, Mitzenmacher, Michael, Yu, Minlan, Antichi, Gianni
Fine-grained network telemetry is becoming a modern datacenter standard and is the basis of essential applications such as congestion control, load balancing, and advanced troubleshooting. As network size increases and telemetry gets more fine-graine
Externí odkaz:
http://arxiv.org/abs/2202.02270
Stream monitoring is fundamental in many data stream applications, such as financial data trackers, security, anomaly detection, and load balancing. In that respect, quantiles are of particular interest, as they often capture the user's utility. For
Externí odkaz:
http://arxiv.org/abs/2201.01958
Autor:
Langlet, Jonatan, Basat, Ran Ben, Ramanathan, Sivaramakrishnan, Oliaro, Gabriele, Mitzenmacher, Michael, Yu, Minlan, Antichi, Gianni
Programmable switches are driving a massive increase in fine-grained measurements. This puts significant pressure on telemetry collectors that have to process reports from many switches. Past research acknowledged this problem by either improving col
Externí odkaz:
http://arxiv.org/abs/2110.05438
Autor:
Vargaftik, Shay, Basat, Ran Ben, Portnoy, Amit, Mendelson, Gal, Ben-Itzhak, Yaniv, Mitzenmacher, Michael
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients send local gradients to a parameter server for averaging and updating the model. Due to communication constraints, clients often use lossy compression
Externí odkaz:
http://arxiv.org/abs/2108.08842
Autor:
Vargaftik, Shay, Basat, Ran Ben, Portnoy, Amit, Mendelson, Gal, Ben-Itzhak, Yaniv, Mitzenmacher, Michael
We consider the problem where $n$ clients transmit $d$-dimensional real-valued vectors using $d(1+o(1))$ bits each, in a manner that allows the receiver to approximately reconstruct their mean. Such compression problems naturally arise in distributed
Externí odkaz:
http://arxiv.org/abs/2105.08339
Counters are the fundamental building block of many data sketching schemes, which hash items to a small number of counters and account for collisions to provide good approximations for frequencies and other measures. Most existing methods rely on fix
Externí odkaz:
http://arxiv.org/abs/2102.12531