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Locally Encoded Secure Distributed Batch Matrix Multiplication.

Haobo Jia¹, Zhuqing Jia¹

¹School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Entropy (Basel, Switzerland)

|December 24, 2025

Summary

This summary is machine-generated.

This study introduces a new method for secure distributed matrix multiplication, enabling efficient batch processing even with unreliable worker nodes. The scheme ensures data privacy while tolerating system delays and malicious participants.

Keywords:

batch matrix multiplication communication and computation efficiency local encoding pattern secure distributed computating straggler tolerance

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Area of Science:

Distributed computing
Information theory
Cryptography

Background:

Batch matrix multiplication is computationally intensive.
Distributed systems face challenges with stragglers (slow nodes) and colluding nodes.
Existing methods for coded distributed batch matrix multiplication (CDBMM) have limitations in secure, multi-batch scenarios.

Purpose of the Study:

To develop the first scheme for locally encoded secure distributed batch matrix multiplication (LESDBMM).
To enable efficient and secure computation of multiple batches of matrix products in a distributed setting.
To handle stragglers and colluding workers while maintaining data privacy.

Main Methods:

Utilizes cross-subspace (CSA) codes and CSA null shaper.
Proposes a novel scheme for LESDBMM with batch processing capabilities.
Analyzes performance in terms of tolerable stragglers, communication, and computation.

Main Results:

The proposed LESDBMM scheme is the first of its kind for batch processing.
Achieves performance comparable to CSA codes for CDBMM when M=1 and X=0.
Demonstrates effective handling of stragglers and colluding workers.

Conclusions:

The developed scheme generalizes CSA codes for CDBMM to the LESDBMM setting.
Offers a robust solution for secure and efficient large-scale matrix computations.
Advances the field of coded distributed computing for complex matrix operations.