Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park

The integration of digital twin(DT)and 6G edge intelligence provides accurate forecasting for distributed resources control in smart park.However,the adverse impact of model poisoning attacks on DT model training cannot be ignored.To address this issue,we firstly construct the models of DT model training and model poisoning attacks.An optimization problem is formulated to minimize the weighted sum of the DT loss function and DT model training delay.Then,the problem is transformed and solved by the proposed Multi-timescAle endogenouS securiTy-aware DQN-based rEsouRce management algorithm(MASTER)based on DT-assisted state information evaluation and attack detection.MASTER adopts multi-timescale deep Q-learning(DQN)networks to jointly schedule local training epochs and devices.It actively adjusts resource management strategies based on estimated attack probability to achieve endogenous security awareness.Simulation results demonstrate that MASTER has excellent performances in DT model training accuracy and delay.

Federated unsupervised representation learning

To leverage the enormous amount of unlabeled data on distributed edge devices,we formulate a new problem in federated learning called federated unsupervised representation learning(FURL)to learn a common representation model without supervision while preserving data privacy.FURL poses two new challenges:(1)data distribution shift(non-independent and identically distributed,non-IID)among clients would make local models focus on different categories,leading to the inconsistency of representation spaces;(2)without unified information among the clients in FURL,the representations across clients would be misaligned.To address these challenges,we propose the federated contrastive averaging with dictionary and alignment(FedCA)algorithm.FedCA is composed of two key modules:a dictionary module to aggregate the representations of samples from each client which can be shared with all clients for consistency of representation space and an alignment module to align the representation of each client on a base model trained on public data.We adopt the contrastive approach for local model training.Through extensive experiments with three evaluation protocols in IID and non-IID settings,we demonstrate that FedCA outperforms all baselines with significant margins.