activity
20182021
most citedPrivate Federated Learning with Domain Adaptation

57 citations · 57 across the 2 of their papers we have counts for

collaborators

7 papers

cs.LG2021

Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions

Pallika Kanani, Virendra J. Marathe, Daniel Peterson +2

Federated Learning (FL) is quickly becoming a goto distributed training paradigm for users to jointly train a global model without physically sharing their data. Users can indirect…

cs.DC2020

Microsecond Consensus for Microsecond Applications

Marcos K. Aguilera, Naama Ben-David, Rachid Guerraoui +3

We consider the problem of making apps fault-tolerant through replication, when apps operate at the microsecond scale, as in finance, embedded computing, and microservices apps. Th…

cs.DC2020

Efficient Multi-word Compare and Swap

Rachid Guerraoui, Alex Kogan, Virendra J. Marathe +1

Atomic lock-free multi-word compare-and-swap (MCAS) is a powerful tool for designing concurrent algorithms. Yet, its widespread usage has been limited because lock-free implementat…

cs.LG201957 cited

Private Federated Learning with Domain Adaptation

Daniel Peterson, Pallika Kanani, Virendra J. Marathe

Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other p…

cs.DC2019

Correct, Fast Remote Persistence

Sanidhya Kashyap, Dai Qin, Steve Byan +2

Persistence of updates to remote byte-addressable persistent memory (PM), using RDMA operations (RDMA updates), is a poorly understood subject. Visibility of RDMA updates on the re…

cs.DC2019

The Impact of RDMA on Agreement

Marcos K. Aguilera, Naama Ben-David, Rachid Guerraoui +2

Remote Direct Memory Access (RDMA) is becoming widely available in data centers. This technology allows a process to directly read and write the memory of a remote host, with a mec…