Towards Effective Device-Aware Federated Learning

In Proceedings of the 18th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2019) - November 2019
Download the publication : 2019_Anelli_AIIA2019.pdf [208Ko]  
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users’ personal privacy and data security. To address the above issues, Federated Learning (FL) has been recently proposed as a means to leave data and computational resources distributed over a large number of nodes (clients) where a central coordinating server aggregates only locally computed updates without knowing the original data. In this work, we extend the FL framework by pushing forward the state the art in the field on several dimensions: (i) unlike the original FedAvg approach relying solely on single criteria (i.e., local dataset size), a suite of domain- and client-specific criteria constitute the basis to compute each local client’s contribution, (ii) the multi-criteria contribution of each device is computed in a prioritized fashion by leveraging a priority-aware aggregation operator used in the field of information retrieval, and (iii) a mechanism is proposed for online-adjustment of the aggregation operator parameters via a local search strategy with backtracking. Extensive experiments on a publicly available dataset indicate the merits of the proposed approach compared to standard FedAvg baseline

BibTex references


@InProceedings{ADDF19,
author = {Vito Walter Anelli and Yashar Deldjoo and Tommaso {Di Noia} and Antonio Ferrara},
title = "Towards Effective Device-Aware Federated Learning",
booktitle = "In Proceedings of the 18th International
Conference of the Italian Association for
Artificial Intelligence (AI*IA 2019)",
month = "November",
year = "2019",
publisher = "Springer",
keywords = "federated learning; aggregation; data distribution",
url = "http://www-ictserv.poliba.it/publications/2019/ADD
F19"
}

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