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TinyFedTL: Federated Transfer Learning on Tiny Devices

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arxiv 2110.01107 v1 pith:FQHWEDHJ submitted 2021-10-03 cs.LG cs.DC

Kavya Kopparapu , Eric Lin This is my paper · ORCID
classification cs.LGcs.DC
keywords datafederatedlearningtinyfedtltinymltransferadditionaddress
verification ladder T0 review T1 audit T2 compute T3 formal
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TinyML has rose to popularity in an era where data is everywhere. However, the data that is in most demand is subject to strict privacy and security guarantees. In addition, the deployment of TinyML hardware in the real world has significant memory and communication constraints that traditional ML fails to address. In light of these challenges, we present TinyFedTL, the first implementation of federated transfer learning on a resource-constrained microcontroller.

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Cited by 1 Pith paper

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  1. TIFeD: a Tiny Integer-based Federated learning algorithm with Direct feedback alignment

    cs.LG 2024-11 conditional novelty 4.0 of 10

    TIFeD trains neural networks on tiny devices with integer-only federated learning based on direct feedback alignment, including a variant where each device updates only one layer.

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