Fed-OGD: Mitigating Straggler Effects in Federated Learning via Orthogonal Gradient Descent
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DOI: 10.1109/tc.2025.3584272
Abstract: Federated Learning (FL) faces challenges due to straggler clients that impede timely parameter uploads, potentially leading to suboptimal global model performance. Existing approaches using synchronous and asynchronous communication suffer from long waiting times or convergence… read more here.
Keywords: gradient descent; fed ogd; straggler; orthogonal gradient ... See more keywords
Distributed Matrix Multiplication With Straggler Tolerance Using Algebraic Function Fields
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DOI: 10.1109/tit.2024.3513693
Abstract: The problem of straggler mitigation in distributed matrix multiplication (DMM) is considered for a large number of worker nodes and a fixed small finite field. Polynomial codes and matdot codes are generalized by making use… read more here.
Keywords: straggler; distributed matrix; function fields; algebraic function ... See more keywords
Straggler-Aware Distributed Learning: Communication–Computation Latency Trade-Off
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DOI: 10.3390/e22050544
Abstract: When gradient descent (GD) is scaled to many parallel workers for large-scale machine learning applications, its per-iteration computation time is limited by straggling workers. Straggling workers can be tolerated by assigning redundant computations and/or coding… read more here.
Keywords: latency; per iteration; learning; computation ... See more keywords