Author(s)

Arjun Malhotra^*, Felix Baumann

Affiliation(s)

Department of Computer Science, University of British Columbia, Vancouver, Canada.

Corresponding Author

Arjun Malhotra

ABSTRACT

The proliferation of Internet of Things applications and latency-sensitive computing tasks has necessitated novel approaches to resource management across distributed edge-cloud architectures. Federated Learning has emerged as a compelling paradigm for collaborative model training without centralizing data, yet integrating Federated Learning into multi-objective scheduling frameworks presents significant challenges. This paper proposes a comprehensive scheduling strategy that accounts for Federated Learning-specific requirements including model convergence time, communication overhead, and data heterogeneity while optimizing multiple conflicting objectives such as makespan, energy consumption, and resource utilization. We develop a hierarchical scheduling architecture that coordinates tasks between mobile edge computing servers, base stations, and cloud data centers while maintaining Federated Learning protocol integrity. The proposed approach employs an adaptive multi-objective optimization algorithm with dynamic crossover and mutation probabilities that adjusts resource allocation based on Federated Learning training progress and system state. Experimental evaluation demonstrates that our Federated Learning-aware scheduling strategy with adaptive genetic algorithm parameters achieves superior performance compared to fixed-parameter approaches, reducing total system cost by approximately 28 percent while improving convergence speed by 35 percent. The framework effectively balances computation offloading decisions with Federated Learning communication patterns across the hierarchical mobile edge computing infrastructure, resulting in enhanced system efficiency for distributed edge-cloud environments. This work establishes foundations for integrating Federated Learning workflows into production-scale distributed computing infrastructures while addressing the unique challenges posed by privacy-preserving machine learning paradigms.

KEYWORDS

Federated learning; Multi-objective optimization; Task scheduling; Edge computing; Cloud computing; Mobile edge computing; Adaptive genetic algorithm

CITE THIS PAPER

Arjun Malhotra, Felix Baumann. Federated learning-aware multi-objective scheduling for distributed edge-cloud environments. AI and Data Science Journal. 2025, 2(3): 35-46. DOI: https://doi.org/10.61784/adsj3031.

REFERENCES

[1] Konakanchi S. Edge computing for latency-sensitive AI applications. Global Perspectives on Multidisciplinary Research, 2025, 6(1): 53-63.

[2] Chen M, Yang Z, Saad W, et al. A joint learning and communications framework for federated learning over wireless networks. IEEE Transactions on Wireless Communications, 2021, 20(1): 269-283.

[3] Kong L, Tan J, Huang J, et al. Edge-computing-driven internet of things: A survey. ACM Computing Surveys, 2022, 55(8): 1-41.

[4] Yang S, Ding G, Chen Z, et al. GART: Graph neural network-based adaptive and robust task scheduler for heterogeneous distributed computing. IEEE Access, 2025, 13, 200196-200216. DOI: 10.1109/ACCESS.2025.3633290.

[5] Park J, Samarakoon S, Elgabli A, et al. Communication-efficient and distributed learning over wireless networks: Principles and applications. Proceedings of the IEEE, 2021, 109(5): 796-819.

[6] Li T, Sahu AK, Zaheer M, et al. Federated optimization in heterogeneous networks. Machine Learning and Systems, 2020, 429-450.

[7] Zhang Z, Panda A, Song L, et al. Neurotoxin: Durable backdoors in federated learning. International Conference on Machine Learning, 2022, 26429-26446.

[8] Malekimajd M, Safarpoor-Dehkordi A. A survey on cloud computing scheduling algorithms. Multiagent and Grid Systems, 2022, 18(2): 119-148.

[9] Li Q, Wen Z, Wu Z, et al. A survey on federated learning systems: Vision, hype and reality for data privacy and protection. IEEE Transactions on Knowledge and Data Engineering, 2021, 35(4): 3347-3366.

[10] Xu J, Wang H, Chen L. Bandwidth allocation for multiple federated learning services in wireless edge networks. IEEE Transactions on Wireless Communications, 2021, 21(4): 2534-2546.

[11] Zhou Z, Chen X, Li E, et al. Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 2019, 107(8): 1738-1762.

[12] Lim WYB, Luong NC, Hoang DT, et al. Federated learning in mobile edge networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 2020, 22(3): 2031-2063.

[13] Bonawitz K, Eichner H, Grieskamp W, et al. Towards federated learning at scale: System design. Machine Learning and Systems, 2019, 374-388.

[14] Yadav D, Ramu P, Deb K. Multi-objective robust optimization and decision-making using evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, 2023, 786-794.

[15] Khan MA, Khan M, Naeem R, et al. Synergistic effect of Citrus sinensis and Aloe barbadensis extracts on biosynthesis and antimicrobial activities of zinc oxide nanoparticles. Pakistan Journal of Botany, 2024, 56(4): 1373-1378. DOI: 10.30848/PJB2024-4(9).

[16] Sheikh F, Naz I, Rahman L, et al. Genus Fagonia mediated nanoparticles and their therapeutic potential: A review. Pakistan Journal of Botany, 2024, 56(4): 1623-1629. DOI: 10.30848/PJB2024-4(13).

[17] He Z, Yang L, Lin W, et al. Improving accuracy and convergence in group-based federated learning on non-IID data. IEEE Transactions on Network Science and Engineering, 2022, 10(3): 1389-1404.

[18] Kairouz P, McMahan HB, Avent B, et al. Advances and open problems in federated learning. Foundations and Trends in Machine Learning, 2021, 14(1–2): 1-210.

[19] Chen Z, Wang Y, Zhao X. Responsible generative AI: Governance challenges and solutions in enterprise data clouds. Journal of Computing and Electronic Information Management, 2025, 18(3): 59-65.

[20] Zeng Z, Yang S, Ding G. Robust aggregation algorithms for federated learning in unreliable network environments. Journal of Computing and Electronic Information Management, 2025, 18(3): 34-42.

[21] Chen J, Cui Y, Zhang X, et al. Temporal convolutional network for carbon tax projection: A data-driven approach. Applied Sciences, 2024, 14(20): 9213.

[22] Cui Y, Han X, Chen J, et al. FraudGNN-RL: A graph neural network with reinforcement learning for adaptive financial fraud detection. IEEE Open Journal of the Computer Society, 2025, 6, 426-437. DOI: 10.1109/OJCS.2025.3543450.

[23] Mai NT, Cao W, Fang Q. A study on how LLMs (e.g. GPT-4, chatbots) are being integrated to support tutoring, essay feedback and content generation. Journal of Computing and Electronic Information Management, 2025, 18(3): 43-52.

[24] Ali N, Ullah Z, Ali A, et al. Taxonomic studies of Cyperaceae (sedges) in Swat Pakistan: Using a multivariate approach. Pakistan Journal of Botany, 2024, 56(4): 1517-1531. DOI: 10.30848/PJB2024-4(19).

[25] Lin H, Liu W. Symmetry-aware causal-inference-driven web performance modeling: A structure-aware framework for predictive analysis and actionable optimization. Symmetry, 2025, 17(12): 2058.

[26] Sankaranarayanan S, Rodrigues JJ, Sugumaran V, et al. Data flow and distributed deep neural network based low latency IoT-edge computation model for big data environment. Engineering Applications of Artificial Intelligence, 2020, 94, 103785.

[27] Mai NT, Fang Q, Cao W. Measuring student trust and over-reliance on AI tutors: Implications for STEM learning outcomes. International Journal of Social Sciences and English Literature, 2025, 9(12): 11-17.

[28] Yang J, Zeng Z, Shen Z. Neural-symbolic dual-indexing architectures for scalable retrieval-augmented generation. IEEE Access, 2025, 13, 210507-210519. DOI: 10.1109/ACCESS.2025.3638761.

[29] Wang Y, Ding G, Zeng Z, et al. Causal-aware multimodal transformer for supply chain demand forecasting: Integrating text, time series, and satellite imagery. IEEE Access, 2025, 13, 176813-176829. DOI: 10.1109/ACCESS.2025.3619552.

[30] Sun T, Yang J, Li J, et al. Enhancing auto insurance risk evaluation with transformer and SHAP. IEEE Access, 2024, 12, 116546-116557. DOI: 10.1109/ACCESS.2024.3446179.

[31] Han X, Yang Y, Chen J, et al. Symmetry-aware credit risk modeling: A deep learning framework exploiting financial data balance and invariance. Symmetry, 2025, 17(3): 341. DOI: 10.3390/sym17030341.