Enhancing Federated Learning with SOA: An Approach to Tackle Non-IID Data Challenges

Loukas Papadopoulos; Nemania Borovits; George Manias; Damien Andrew Tamburri; Willem-Jan A.M. van den Heuvel

doi:10.1007/978-981-96-7238-7_14

Enhancing Federated Learning with SOA: An Approach to Tackle Non-IID Data Challenges

Loukas Papadopoulos
, Nemania Borovits (Corresponding author)
, George Manias
, Damien Andrew Tamburri
, Willem-Jan A.M. van den Heuvel

Data Governance

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Federated Learning (FL) enhances data privacy by training machine learning models across devices without centralizing sensitive data, aligning with GDPR mandates. However, FL faces challenges with Non-Independent and Identically Distributed (Non-IID) data, which affects model performance. To address this, we leverage Service-Oriented Architecture (SOA) principles, specifically the Aggregator pattern, by introducing in-client clustering during FL’s local training phase to boost accuracy. This approach is applied to a Named Entity Recognition (NER) task in the medical domain using ADE Corpus and CADEC datasets, with further evaluation on the general-purpose CoNLL dataset for generalizability. Results show improvements in weighted F1-scores: 3.5% for ADE Corpus, 1.4% for CADEC and a marginal gain for CoNLL, highlighting SOA’s potential in optimizing FL. These findings encourage future exploration of SOA principles in FL, offering promising solutions for distributed learning challenges.

Original language	English
Title of host publication	Service-Oriented Computing – ICSOC 2024 Workshops
Subtitle of host publication	ASOCA, AI-PA, WESOACS, GAISS, LAIS, AI on Edge, RTSEMS, SQS, SOCAISA, SOC4AI and Satellite Events, Tunis, Tunisia, December 3–6, 2024, Revised Selected Papers, Part I
Editors	Slim Kallel, Claudia Raibulet, Ismael Bouassida Rodriguez, Noura Faci, Amel Bennaceur, Saoussen Cheikhrouhou, Leila Ben Ayed, Mohamed Sellami, Elisa Yumi Nakagawa, Riadh Ben Halima
Place of Publication	Singapore
Publisher	Springer
Pages	169-181
Number of pages	13
ISBN (Electronic)	978-981-96-7238-7
ISBN (Print)	978-981-96-7237-0
DOIs	https://doi.org/10.1007/978-981-96-7238-7_14
Publication status	Published - 23 Jul 2025
Event	22nd International Conference on Service-Oriented Computing, ICSOC 2024 - Tunis, Tunisia Duration: 3 Dec 2024 → 6 Dec 2024

Publication series

Name	Lecture Notes in Computer Science (LNCS)
Volume	15833
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Service-Oriented Computing, ICSOC 2024
Abbreviated title	ICSOC
Country/Territory	Tunisia
City	Tunis
Period	3/12/24 → 6/12/24

Funding

The research leading to the results presented in this paper has received funding from the European Union’s Horizon Europe research and innovation program under grant agreement no 101137301 and is supported by the Innovative UK under grant agreement no 10103541.

Keywords

Federated Learning
In-client Clustering
Named Entity Recognition

Access to Document

10.1007/978-981-96-7238-7_14

Cite this

Papadopoulos, L., Borovits, N., Manias, G., Tamburri, D. A., & van den Heuvel, W.-J. A. M. (2025). Enhancing Federated Learning with SOA: An Approach to Tackle Non-IID Data Challenges. In S. Kallel, C. Raibulet, I. B. Rodriguez, N. Faci, A. Bennaceur, S. Cheikhrouhou, L. Ben Ayed, M. Sellami, E. Y. Nakagawa, & R. Ben Halima (Eds.), Service-Oriented Computing – ICSOC 2024 Workshops: ASOCA, AI-PA, WESOACS, GAISS, LAIS, AI on Edge, RTSEMS, SQS, SOCAISA, SOC4AI and Satellite Events, Tunis, Tunisia, December 3–6, 2024, Revised Selected Papers, Part I (pp. 169-181). (Lecture Notes in Computer Science (LNCS); Vol. 15833). Springer. https://doi.org/10.1007/978-981-96-7238-7_14

Papadopoulos, Loukas ; Borovits, Nemania ; Manias, George et al. / Enhancing Federated Learning with SOA : An Approach to Tackle Non-IID Data Challenges. Service-Oriented Computing – ICSOC 2024 Workshops: ASOCA, AI-PA, WESOACS, GAISS, LAIS, AI on Edge, RTSEMS, SQS, SOCAISA, SOC4AI and Satellite Events, Tunis, Tunisia, December 3–6, 2024, Revised Selected Papers, Part I. editor / Slim Kallel ; Claudia Raibulet ; Ismael Bouassida Rodriguez ; Noura Faci ; Amel Bennaceur ; Saoussen Cheikhrouhou ; Leila Ben Ayed ; Mohamed Sellami ; Elisa Yumi Nakagawa ; Riadh Ben Halima. Singapore : Springer, 2025. pp. 169-181 (Lecture Notes in Computer Science (LNCS)).

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title = "Enhancing Federated Learning with SOA: An Approach to Tackle Non-IID Data Challenges",

abstract = "Federated Learning (FL) enhances data privacy by training machine learning models across devices without centralizing sensitive data, aligning with GDPR mandates. However, FL faces challenges with Non-Independent and Identically Distributed (Non-IID) data, which affects model performance. To address this, we leverage Service-Oriented Architecture (SOA) principles, specifically the Aggregator pattern, by introducing in-client clustering during FL{\textquoteright}s local training phase to boost accuracy. This approach is applied to a Named Entity Recognition (NER) task in the medical domain using ADE Corpus and CADEC datasets, with further evaluation on the general-purpose CoNLL dataset for generalizability. Results show improvements in weighted F1-scores: 3.5\% for ADE Corpus, 1.4\% for CADEC and a marginal gain for CoNLL, highlighting SOA{\textquoteright}s potential in optimizing FL. These findings encourage future exploration of SOA principles in FL, offering promising solutions for distributed learning challenges.",

keywords = "Federated Learning, In-client Clustering, Named Entity Recognition",

author = "Loukas Papadopoulos and Nemania Borovits and George Manias and Tamburri, \{Damien Andrew\} and \{van den Heuvel\}, \{Willem-Jan A.M.\}",

year = "2025",

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language = "English",

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pages = "169--181",

editor = "Slim Kallel and Claudia Raibulet and Rodriguez, \{Ismael Bouassida\} and Noura Faci and Amel Bennaceur and Saoussen Cheikhrouhou and \{Ben Ayed\}, Leila and Mohamed Sellami and Nakagawa, \{Elisa Yumi\} and \{Ben Halima\}, Riadh",

booktitle = "Service-Oriented Computing – ICSOC 2024 Workshops",

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note = "22nd International Conference on<br/>Service-Oriented Computing, ICSOC 2024, ICSOC ; Conference date: 03-12-2024 Through 06-12-2024",

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Papadopoulos, L, Borovits, N, Manias, G, Tamburri, DA & van den Heuvel, W-JAM 2025, Enhancing Federated Learning with SOA: An Approach to Tackle Non-IID Data Challenges. in S Kallel, C Raibulet, IB Rodriguez, N Faci, A Bennaceur, S Cheikhrouhou, L Ben Ayed, M Sellami, EY Nakagawa & R Ben Halima (eds), Service-Oriented Computing – ICSOC 2024 Workshops: ASOCA, AI-PA, WESOACS, GAISS, LAIS, AI on Edge, RTSEMS, SQS, SOCAISA, SOC4AI and Satellite Events, Tunis, Tunisia, December 3–6, 2024, Revised Selected Papers, Part I. Lecture Notes in Computer Science (LNCS), vol. 15833, Springer, Singapore, pp. 169-181, 22nd International Conference on
Service-Oriented Computing, ICSOC 2024, Tunis, Tunisia, 3/12/24. https://doi.org/10.1007/978-981-96-7238-7_14

Enhancing Federated Learning with SOA: An Approach to Tackle Non-IID Data Challenges. / Papadopoulos, Loukas; Borovits, Nemania (Corresponding author); Manias, George et al.
Service-Oriented Computing – ICSOC 2024 Workshops: ASOCA, AI-PA, WESOACS, GAISS, LAIS, AI on Edge, RTSEMS, SQS, SOCAISA, SOC4AI and Satellite Events, Tunis, Tunisia, December 3–6, 2024, Revised Selected Papers, Part I. ed. / Slim Kallel; Claudia Raibulet; Ismael Bouassida Rodriguez; Noura Faci; Amel Bennaceur; Saoussen Cheikhrouhou; Leila Ben Ayed; Mohamed Sellami; Elisa Yumi Nakagawa; Riadh Ben Halima. Singapore: Springer, 2025. p. 169-181 (Lecture Notes in Computer Science (LNCS); Vol. 15833).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Enhancing Federated Learning with SOA

T2 - 22nd International Conference on<br/>Service-Oriented Computing, ICSOC 2024

AU - Papadopoulos, Loukas

AU - Borovits, Nemania

AU - Manias, George

AU - Tamburri, Damien Andrew

AU - van den Heuvel, Willem-Jan A.M.

PY - 2025/7/23

Y1 - 2025/7/23

N2 - Federated Learning (FL) enhances data privacy by training machine learning models across devices without centralizing sensitive data, aligning with GDPR mandates. However, FL faces challenges with Non-Independent and Identically Distributed (Non-IID) data, which affects model performance. To address this, we leverage Service-Oriented Architecture (SOA) principles, specifically the Aggregator pattern, by introducing in-client clustering during FL’s local training phase to boost accuracy. This approach is applied to a Named Entity Recognition (NER) task in the medical domain using ADE Corpus and CADEC datasets, with further evaluation on the general-purpose CoNLL dataset for generalizability. Results show improvements in weighted F1-scores: 3.5% for ADE Corpus, 1.4% for CADEC and a marginal gain for CoNLL, highlighting SOA’s potential in optimizing FL. These findings encourage future exploration of SOA principles in FL, offering promising solutions for distributed learning challenges.

AB - Federated Learning (FL) enhances data privacy by training machine learning models across devices without centralizing sensitive data, aligning with GDPR mandates. However, FL faces challenges with Non-Independent and Identically Distributed (Non-IID) data, which affects model performance. To address this, we leverage Service-Oriented Architecture (SOA) principles, specifically the Aggregator pattern, by introducing in-client clustering during FL’s local training phase to boost accuracy. This approach is applied to a Named Entity Recognition (NER) task in the medical domain using ADE Corpus and CADEC datasets, with further evaluation on the general-purpose CoNLL dataset for generalizability. Results show improvements in weighted F1-scores: 3.5% for ADE Corpus, 1.4% for CADEC and a marginal gain for CoNLL, highlighting SOA’s potential in optimizing FL. These findings encourage future exploration of SOA principles in FL, offering promising solutions for distributed learning challenges.

KW - Federated Learning

KW - In-client Clustering

KW - Named Entity Recognition

UR - https://www.scopus.com/pages/publications/105021008621

U2 - 10.1007/978-981-96-7238-7_14

DO - 10.1007/978-981-96-7238-7_14

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A2 - Ben Ayed, Leila

A2 - Sellami, Mohamed

A2 - Nakagawa, Elisa Yumi

A2 - Ben Halima, Riadh

PB - Springer

CY - Singapore

Y2 - 3 December 2024 through 6 December 2024

ER -

Papadopoulos L, Borovits N, Manias G, Tamburri DA , van den Heuvel WJAM. Enhancing Federated Learning with SOA: An Approach to Tackle Non-IID Data Challenges. In Kallel S, Raibulet C, Rodriguez IB, Faci N, Bennaceur A, Cheikhrouhou S, Ben Ayed L, Sellami M, Nakagawa EY, Ben Halima R, editors, Service-Oriented Computing – ICSOC 2024 Workshops: ASOCA, AI-PA, WESOACS, GAISS, LAIS, AI on Edge, RTSEMS, SQS, SOCAISA, SOC4AI and Satellite Events, Tunis, Tunisia, December 3–6, 2024, Revised Selected Papers, Part I. Singapore: Springer. 2025. p. 169-181. (Lecture Notes in Computer Science (LNCS)). doi: 10.1007/978-981-96-7238-7_14

Enhancing Federated Learning with SOA: An Approach to Tackle Non-IID Data Challenges

Abstract

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Keywords

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