Robust Privacy-Preserving Federated Learning for 6G Network Resource Allocation
Download| Volume 7 Issue 1, 2026 | |
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| Author(s): |
Nizamuddin Maitlo* Institute of Computer Science, Shah Abdul Latif University, Khairpur, nizamuddin.cs@gmail.com Samina Rajper Institute of Computer Science, Shah Abdul Latif University, Khairpur, samina.rajper@salu.edu.pk Manahil Shaikh Institute of Computer Science, Shah Abdul Latif University, Khairpur, manahilshaikh36@gmail.com Hina Kareem Institute of Computer Science, Shah Abdul Latif University, Khairpuri, hinakareem.mahar@gmail.com |
| Abstract | Sixth-generation (6G) radio access networks must allocate resources across heterogeneous service slices—enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communication (URLLC), massive Machine-Type Communication (mMTC), and Extended Reality (XR)—while meeting strict service-level targets and preserving privacy. We present a robust, privacy-compatible federated learning (FL) pipeline that learns slice-aware admission and serving decisions from gNB-side telemetry without centralizing raw logs. Using eight non-IID clients with 150 telemetry windows per client sampled every 90 s, we train a compact client-side classifier for 20 global rounds under partial participation (approximately 85%). Labels are defined by next-window SLA satisfaction (downlink throughput ≥ 40 Mbps and p95 latency ≤ 50 ms). The global model improves from 0.63 to 0.91 accuracy and from 0.62 to 0.85 macro-F1, while the training loss drops from 0.92 to 0.18. Fairness, measured as the gap between the best and worst mean per-client accuracy, is 0.08, with most per-round gaps falling in the 0.05–0.14 range. A heterogeneity snapshot confirms strong non-IID conditions across PRB utilization, CQI, p95 latency, UE density, and slice-mix entropy. The pipeline outputs reproducible CSV logs and figures and is designed to remain compatible with client-level differential privacy (norm clipping with Gaussian noise), secure aggregation, and over-the-air (AirComp) update fusion to reduce uplink time. These results indicate that FL can achieve accurate and fairness-aware 6G resource allocation under realistic participation and stringent SLAs while supporting privacy and communication optimizations. |
| Keywords | 6G, federated learning, radio access networks, network slicing, resource allocation, non-IID data, fairness, differential privacy, secure aggregation, over-the-air computation. |
| Year | 2026 |
| Volume | 7 |
| Issue | 1 |
| Type | Research paper, manuscript, article |
| Recognized by | Higher Education Commission of Pakistan, HEC | Category | Journal Name | ILMA Journal of Technology & Software Management | Publisher Name | ILMA University | Jel Classification | -- | DOI | - | ISSN no (E, Electronic) | 2790-590X | ISSN no (P, Print) | 2709-2240 | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://ijtsm.ilmauniversity.edu.pk/arc/Vol7/i1/pdf1.pdf | Page | 1-8 |