mlsanityData to 7 Oct 2026

A Lightweight and Secure Deep Learning Model for Privacy-Preserving Federated Learning in Intelligent Enterprises

arXiv 2503.02017 ↗First posted 3 Mar 2025cs.CR cs.LGcited by 5

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Abstract (arXiv)

The ever growing Internet of Things (IoT) connections drive a new type of organization, the Intelligent Enterprise. In intelligent enterprises, machine learning based models are adopted to extract insights from data. Due to the efficiency and privacy challenges of these traditional models, a new federated learning (FL) paradigm has emerged. In FL, multiple enterprises can jointly train a model to update a final model. However, firstly, FL trained models usually perform worse than centralized models, especially when enterprises training data is non-IID (Independent and Identically Distributed). Second, due to the centrality of FL and the untrustworthiness of local enterprises, traditional FL solutions are vulnerable to poisoning and inference attacks and violate privacy. Thirdly, the continuous transfer of parameters between enterprises and servers increases communication costs. To this end, the FedAnil+ model is proposed, a novel, lightweight, and secure Federated Deep Learning Model that includes three main phases. In the first phase, the goal is to solve the data type distribution skew challenge. Addressing privacy concerns against poisoning and inference attacks is covered in the second phase. Finally, to alleviate the communication overhead, a novel compression approach is proposed that significantly reduces the size of the updates. The experiment results validate that FedAnil+ is secure against inference and poisoning attacks with better accuracy. In addition, it shows improvements over existing approaches in terms of model accuracy (13%, 16%, and 26%), communication cost (17%, 21%, and 25%), and computation cost (7%, 9%, and 11%).

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 40 references that are in the map. First posted on arXiv 3 Mar 2025.

Builds on

12 of 40 references.

References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2020Adaptive Federated Optimizationcited by 1,036
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  7. 2018LEAF: A Benchmark for Federated Settingscited by 780
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2019Robust Aggregation for Federated Learningcited by 314
  11. 2025Decentralized and Robust Privacy-Preserving Model Using Blockchain-Enabled Federated Deep Learning in Intelligent Enterprisescited by 7
  12. 2023Federated Skewed Label Learning with Logits Fusioncited by 5

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneitycited by 1
  2. 2025ZTFed-MAS2S: A Zero-Trust Federated Learning Framework with Verifiable Privacy and Trust-Aware Aggregation for Wind Power Data Imputationcited by 1
  3. 2026Fractional-Order Federated Learningcited by 0
  4. 2026TITAN-FedAnil+: Trust-Based Adaptive Blockchain Federated Learning for Resource-Constrained Intelligent Enterprisescited by 0
  5. 2026FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weightingcited by 0