Metraipolis › archive › dc_f33bc27773fa

Consensus-Constrained Dual-Federated Optimization with Client-Side Dynamic Weighting for Distributed Android Device Threat Defense — arXiv:2611.09470

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.09470
License (as archived): CC0-1.0
Archived: 2026-11-09T22:36:37.045Z
SHA-256 of the archived text: 6b4fd6a21c8969b36793fdbe0d651ed677a0673f34525ca41528d1ac9d2b9679
Stable URL of this page: https://metraipolis.com/documento/dc_f33bc27773fa

How to cite

Consensus-Constrained Dual-Federated Optimization with Client-Side Dynamic Weighting for Distributed Android Device Threat Defense — arXiv:2611.09470 — arXiv (en), https://arxiv.org/abs/2611.09470 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f33bc27773fa (Metraipolis archive dc_f33bc27773fa, sha256 6b4fd6a21c8969b36793fdbe0d651ed677a0673f34525ca41528d1ac9d2b9679)

BibTeX

@misc{dcf33bc27773fa,
  title = {Consensus-Constrained Dual-Federated Optimization with Client-Side Dynamic Weighting for Distributed Android Device Threat Defense — arXiv:2611.09470},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.09470},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f33bc27773fa; sha256 6b4fd6a21c8969b36793fdbe0d651ed677a0673f34525ca41528d1ac9d2b9679},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.09470. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Smartphones, smart-home hubs, and wearables are increasingly exposed to sophisticated Android malware, while centralized threat-detection pipelines raise privacy and scalability concerns. Federated learning (FL) enables collaborative model training without collecting raw client data, but partial participation and variation in local model quality can destabilize global optimization. In this article, we propose DW-FedADMM, a federated optimization framework that combines primal-dual coordination with performance-dependent client-side weighting. The dynamic coefficient modulates each participating client's local state before the augmented-Lagrangian update, while the dual variables regulate disagreement between local and global model states. We evaluate DW-FedADMM on three Android malware benchmarks, namely Drebin, Malgenome, and TUANDROMD, with 5, 10, and 15 clients holding IID data shards, partial client participation, and communication budgets of 5 and 10 rounds. Each configuration is repeated with ten random seeds. DW-FedADMM obtains the highest seed-averaged accuracy on Drebin (97.64% +/- 0.20%) and the best mean accuracy in 9 of the 18 dataset-client-round configurations, including 7 of the 9 configurations with a 10-round budget. Pooled over all configurations, it reaches 95.42% accuracy, 94.62% F1-score, 96.06% precision, and 99.45% AUC, compared with 95.32%, 94.51%, 95.56%, and 99.41% for FedADMM, while also giving the lowest mean false-positive rate and the smallest client update norm among the six compared methods. Both primal-dual methods are well ahead of the averaging- and proximal-based baselines on Malgenome and TUANDROMD, where the latter often remain at the majority-class solution when clients are fragmented. The gain of dynamic weighting over FedADMM is most pronounced on Drebin and with the longer communication budget, where it also reduces the variation across seeds.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv cs.DC, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Cryptography and Security (cs.CR); Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2611.09470 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-09T22:31:17.071Z dalla pagina https://arxiv.org/list/cs.DC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).