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CFSL-BC: Compression-enabled federated split learning with blockchain for robust android malware detection
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AI
DOI：10.1016/j.comnet.2026.112514.png)
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Abstract
En 中文
Android applications have increased the difficulty of identifying advanced and obfuscated malware due to the rapid proliferation. Available federated and blockchain-based models are typically characterized by high communication overhead, low robustness, and insufficient feature diversity. In the study, Compression-Enabled Federated Split Learning with Blockchain (CFSL-BC) is proposed as a new framework that combines compression, split learning (SL), and blockchain to provide an effective, privacy-protected, and robust blockchain detection. The system also relies on built-in compression to reduce transportation costs, SL during joint model training, and smart contracts, with IPFS providing security features. In addition, Byzantine-robust aggregation and differential privacy defend against both poisoning and leakage attacks of the model. The results of experiments on the CIC-MalDroid 2020, Drebin, and Maloid-DS datasets show that CFSL-BC achieves 98.12% accuracy and incurs over 60% less communication overhead than the traditional federated framework. These findings validate CFSL-BC as a feasible and safe mechanism for detecting next-generation malicious Android software. Future research will focus on adaptive compression techniques, cross-domain applications in IoT systems, and a blockchain-based incentive scheme to further enhance the scalability, energy efficiency, and trustworthiness of participating nodes.
Keywords:
Federated split learning
Blockchain
Gradient compression
Android malware detection
Differential privacy
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