250 citations · 1.1k across the 76 of their papers we have counts for
7 papers · 1 filter
Zero-CPU Collection with Direct Telemetry Access
Jonatan Langlet, Ran Ben Basat, Sivaramakrishnan Ramanathan +4
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 swi…
Uniform Bounds for Scheduling with Job Size Estimates
Ziv Scully, Isaac Grosof, Michael Mitzenmacher
We consider the problem of scheduling to minimize mean response time in M/G/1 queues where only estimated job sizes (processing times) are known to the scheduler, where a job of tr…
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
Shay Vargaftik, Ran Ben Basat, Amit Portnoy +3
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.…
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
Maximilian Lam, Gu-Yeon Wei, David Brooks +2
We show that aggregated model updates in federated learning may be insecure. An untrusted central server may disaggregate user updates from sums of updates across participants give…
DRIVE: One-bit Distributed Mean Estimation
Shay Vargaftik, Ran Ben Basat, Amit Portnoy +3
We consider the problem where clients transmit -dimensional real-valued vectors using bits each, in a manner that allows the receiver to approximately reconstruc…
SALSA: Self-Adjusting Lean Streaming Analytics
Ran Ben Basat, Gil Einziger, Michael Mitzenmacher +1
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…