Our study is to confirm the importance of uploading the difference of weights in sparsification and to observe how different the accuracy between the two ...
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In this section, we conduct accuracy comparison of the two schemes in three different ways: by epochs, by sparsi- fication rate, and by BSC error rate, ...
This study is to confirm the importance of uploading the difference of weights in sparsification and to observe how different the accuracy between the two ...
Our empirical study shows that FedLAMA reduces the communication cost by up to 60% for IID data and 70% for non-IID data while achieving a comparable accuracy ...
Dive into the research topics of 'Sparsification on Different Federated Learning Schemes: Comparative Analysis'. Together they form a unique fingerprint.
Sparsification on Different Federated Learning Schemes: Comparative Analysis ; dc.description.abstract, High communication overhead is a major bottleneck in ...
NeFL: Nested Federated Learning for Heterogeneous Clients. CoRR abs ... Sparsification on Different Federated Learning Schemes: Comparative Analysis.
By integrating different sparsification operators with model perturbation, we design two algorithms of. Fed-SMP: Fed-SMP with unbiased random sparsi ...
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We present FedSparsify, a sparsification strategy for federated training based on progressive weight magnitude pruning, which provides several benefits.
We aim at evaluating the performance of Fed-SPA with different levels of compression and comparing them with the performance of the following three schemes ...