Blind federated learning at the wireless edge with low-resolution ADC and DAC
buir.contributor.author | Teğin, Büşra | |
buir.contributor.orcid | Teğin, Büşra|0000-0002-3342-5414 | |
dc.citation.epage | 7798 | en_US |
dc.citation.issueNumber | 12 | en_US |
dc.citation.spage | 7786 | en_US |
dc.citation.volumeNumber | 20 | en_US |
dc.contributor.author | Teğin, Büşra | |
dc.date.accessioned | 2022-02-05T11:21:50Z | |
dc.date.available | 2022-02-05T11:21:50Z | |
dc.date.issued | 2021-06-15 | |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description.abstract | We study collaborative machine learning systems where a massive dataset is distributed across independent workers which compute their local gradient estimates based on their own datasets. Workers send their estimates through a multipath fading multiple access channel with orthogonal frequency division multiplexing to mitigate the frequency selectivity of the channel. We assume that there is no channel state information (CSI) at the workers, and the parameter server (PS) employs multiple antennas to align the received signals. To reduce the power consumption and the hardware costs, we employ complex-valued low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs), at the transmitter and the receiver sides, respectively, and study the effects of practical low-cost DACs and ADCs on the learning performance. Our theoretical analysis shows that the impairments caused by low-resolution DACs and ADCs, including those of one-bit DACs and ADCs, do not prevent the convergence of the federated learning algorithms, and the multipath channel effects vanish when a sufficient number of antennas are used at the PS. We also validate our theoretical results via simulations, and demonstrate that using low-resolution, even one-bit, DACs and ADCs causes only a slight decrease in the learning accuracy. | en_US |
dc.description.provenance | Submitted by Evrim Ergin (eergin@bilkent.edu.tr) on 2022-02-05T11:21:50Z No. of bitstreams: 1 Blind_federated_learning_at_the_wireless_edge_with_low-resolution_ADC_and_DAC.pdf: 1489967 bytes, checksum: e8d5797d79712c54333616effc4b23ab (MD5) | en |
dc.description.provenance | Made available in DSpace on 2022-02-05T11:21:50Z (GMT). No. of bitstreams: 1 Blind_federated_learning_at_the_wireless_edge_with_low-resolution_ADC_and_DAC.pdf: 1489967 bytes, checksum: e8d5797d79712c54333616effc4b23ab (MD5) Previous issue date: 2021-06-15 | en |
dc.identifier.doi | 10.1109/TWC.2021.3087594 | en_US |
dc.identifier.eissn | 1558-2248 | |
dc.identifier.issn | 1536-1276 | |
dc.identifier.uri | http://hdl.handle.net/11693/77091 | |
dc.language.iso | English | en_US |
dc.publisher | IEEE | en_US |
dc.relation.isversionof | https://doi.org/10.1109/TWC.2021.3087594 | en_US |
dc.source.title | IEEE Transactions on Wireless Communications | en_US |
dc.subject | Distributed machine learning | en_US |
dc.subject | Federated learning | en_US |
dc.subject | Stochastic gradient descent | en_US |
dc.subject | Wireless channels | en_US |
dc.subject | OFDM | en_US |
dc.subject | Low-resolution DAC and ADC | en_US |
dc.subject | One-bit DAC and ADC | en_US |
dc.title | Blind federated learning at the wireless edge with low-resolution ADC and DAC | en_US |
dc.type | Article | en_US |
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