FLPP: A Federated-Learning-Based Scheme for Privacy Protection in Mobile Edge Computing.

Data sharing and analyzing among different devices in mobile edge computing is valuable for social innovation and development. The limitation to the achievement of this goal is the data privacy risk. Therefore, existing studies mainly focus on enhancing the data privacy-protection capability. On the one hand, direct data leakage is avoided through federated learning by converting raw data into model parameters for transmission. On the other hand, the security of federated learning is further strengthened by privacy-protection techniques to defend against inference attack. However, privacy-protection techniques may reduce the training accuracy of the data while improving the security. Particularly, trading off data security and accuracy is a major challenge in dynamic mobile edge computing scenarios. To address this issue, we propose a federated-learning-based privacy-protection scheme, FLPP. Then, we build a layered adaptive differential privacy model to dynamically adjust the privacy-protection level in different situations. Finally, we design a differential evolutionary algorithm to derive the most suitable privacy-protection policy for achieving the optimal overall performance. The simulation results show that FLPP has an advantage of 8∼34% in overall performance. This demonstrates that our scheme can enable data to be shared securely and accurately.

LERMS: A Low-Latency and Reliable Downlink Packet-Level Encoding Transmission Method in Untrusted 5GA Edge Network.

The increasing demand for end-to-end low-latency and high-reliability transmissions between edge computing nodes and user elements in 5G Advance edge networks has brought new challenges to the transmission of data. In response, this paper proposes LERMS, a packet-level encoding transmission scheme designed for untrusted 5GA edge networks that may encounter malicious transmission situations such as data tampering, discarding, and eavesdropping. LERMS achieves resiliency against such attacks by using 5GA Protocol data unit (PDU) coded Concurrent Multipath Transfer (CMT) based on Lagrangian interpolation and Raptor's two-layer coding, which provides redundancy to eliminate the impact of an attacker's malicious behavior. To mitigate the increased queuing delay resulting from encoding in data blocks, LERMS is queue-aware with variable block length. Its strategy is modeled as a Markov chain and optimized using a matrix method. Numerical results demonstrate that LERMS achieves the optimal trade-off between delay and reliability while providing resiliency against untrusted edge networks.