A4 Artikkeli konferenssijulkaisussa
Incentive Mechanism Design For Federated Learning in Multi-access Edge Computing (2022)

Liu, J., Chang, Z., Min, G., & Han, Z. (2022). Incentive Mechanism Design For Federated Learning in Multi-access Edge Computing. In GLOBECOM 2022 IEEE Global Communications Conference (pp. 3454-3459). IEEE. IEEE Global Communications Conference. https://doi.org/10.1109/GLOBECOM48099.2022.10000933

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Liu, Jingyuan; Chang, Zheng; Min, Geyong; Han, Zhu

Emojulkaisu: GLOBECOM 2022 IEEE Global Communications Conference

Konferenssin paikka ja aika: Rio de Janeiro, Brazil, 4.-8.12.2022

ISBN: 978-1-6654-3541-3

eISBN: 978-1-6654-3540-6

Lehti tai sarja: IEEE Global Communications Conference

ISSN: 2334-0983

eISSN: 2576-6813

Julkaisuvuosi: 2022

Ilmestymispäivä: 11.01.2023

Artikkelin sivunumerot: 3454-3459

Kustantaja: IEEE

Julkaisumaa: Yhdysvallat (USA)

Julkaisun kieli: englanti

DOI: https://doi.org/10.1109/GLOBECOM48099.2022.10000933

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Tiivistelmä

Federated learning (FL) is a type of distributed machine learning in which mobile users can train data locally and send the results to the FL server to update the global model. However, the implementation of FL may be prevented by the self-fish nature of mobile users, as they need to contribute considerable data and computing resources for participating in the FL process. Therefore, it is of importance to design the incentive mechanism to motivate the users to join the FL. In this work, with explicit consideration of the impact of wireless transmission, we design an incentive scheme to facilitate the FL process by investigating interactions between the multi-access edge computing (MEC) server and mobile users in a MEC-based FL system. By using a two-stage Stackelberg game model, we explore the transmission power allocation of the users and reward policy of the MEC server, and then analyze the Stackelberg equilibrium. The simulation results show that our model is effective for different parameter settings and the utility of the MEC server can be increased significantly compared to the baseline.

YSO-asiasanat: koneoppiminen; reunalaskenta; mobiililaitteet; langaton tiedonsiirto; allokointi; peliteoria

Vapaat asiasanat: Federated learning; multi-access edge computing; incentive mechanism; power allocation

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2022

JUFO-taso: 1