activity
20142026
most citedFastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning

113 citations · 229 across the 41 of their papers we have counts for

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Showing 2019Show all

8 papers · 1 filter

cs.PF2019

Download Time Analysis for Distributed Storage Codes with Locality and Availability

Mehmet Fatih Aktas, Swanand Kadhe, Emina Soljanin +1

The paper presents techniques for analyzing the expected download time in distributed storage systems that employ systematic availability codes. These codes provide access to hot d…

cs.LG2019

Communication-Efficient and Byzantine-Robust Distributed Learning with Error Feedback

Avishek Ghosh, Raj Kumar Maity, Swanand Kadhe +2

We develop a communication-efficient distributed learning algorithm that is robust against Byzantine worker machines. We propose and analyze a distributed gradient-descent algorith…

cs.IT2019

On an Equivalence Between Single-Server PIR with Side Information and Locally Recoverable Codes

Swanand Kadhe, Anoosheh Heidarzadeh, Alex Sprintson +1

Private Information Retrieval (PIR) problem has recently attracted a significant interest in the information-theory community. In this problem, a user wants to privately download o…

quant-ph2019★ 20 cited

Logical Clifford Synthesis for Stabilizer Codes

Narayanan Rengaswamy, Robert Calderbank, Swanand Kadhe +1

Quantum error-correcting codes are used to protect qubits involved in quantum computation. This process requires logical operators, acting on protected qubits, to be translated int…

cs.CR2019★ 26 cited

SeF: A Secure Fountain Architecture for Slashing Storage Costs in Blockchains

Swanand Kadhe, Jichan Chung, Kannan Ramchandran

Full nodes, which synchronize the entire blockchain history and independently validate all the blocks, form the backbone of any blockchain network by playing a vital role in ensuri…

cs.IT2019

Gradient Coding Based on Block Designs for Mitigating Adversarial Stragglers

Swanand Kadhe, O. Ozan Koyluoglu, Kannan Ramchandran

Distributed implementations of gradient-based methods, wherein a server distributes gradient computations across worker machines, suffer from slow running machines, called 'straggl…