Federated learning (FL) has emerged as a pivotal paradigm for distributed model training in edge computing (EC), enabling cooperation among numerous Internet of Things devices while safeguarding their data privacy. Despite its successes in machine learning, concerns regarding data security and model fidelity necessitate the efficient unlearning of target device, i.e., federated unlearning (FUN). However, due to resource constraints, device heterogeneity, and non-independent and identically distributed (Non-IID) data, securely eliminating a device’s impact without retraining the model from scratch presents a complex challenge. In response to these challenges, we propose a hierarchical FUN framework, called Hier-FUN. Hier-FUN organizes edge devices into K clusters, each managed by a head device responsible for aggregating local models within the cluster. To expedite both the learning and unlearning processes of Hier-FUN, we design a heuristic algorithm to determine an appropriate value for K based on devices’ data distributions and available resources. In addition, Hier-FUN denies the communication between the server and cluster heads during training, which can constrain the influence sphere of target device and accelerate the unlearning process. We conduct extensive experiments using real-world datasets, and the experimental results illustrate that Hier-FUN can improve test accuracy by 3.19% during the learning phase and achieve a$6.8\times $speedup during unlearning compared with the baseline methods.

The emerging Federated Learning (FL) permits all workers (e.g., mobile devices) to cooperatively train a model using their local data at the network edge. In order to avoid the possible bottleneck of conventional parameter server architecture, the decentralized federated learning (DFL) is developed on the peer-to-peer (P2P) communication. In DFL, model exchanging among workers is usually regarded as an atomic operation, which largely affects the total bandwidth consumption during model training. Given the limited communication resource on workers, model exchanging will pose a great challenge when meeting with the large-scale models. Herein, we propose to let workers exchange theintermediate results, instead of the entire model, with each other. We provide theoretical analysis of DFL based on intermediate result exchanging, which reveals the relationship between the training performance and the exchanging interval (i.e., the number of local updating iterations) of intermediate results. According to the convergence bound, we propose an adaptive exchanging interval (or frequency) algorithm called Fed-IR, which optimizes the trade-off between communication cost and training performance. Extensive simulation results show that compared with the model exchanging methods, our proposed algorithms can save communication traffic of around 42%$\sim$81% while still achieving the similar accuracy.

The emerging Federated Learning (FL) permits all workers (e.g., mobile devices) to cooperatively train a model using their local data at the network edge. In order to avoid the possible bottleneck of conventional parameter server architecture, the decentralized federated learning (DFL) is developed on the peer-to-peer (P2P) communication. Non-IID issue is a key challenge in FL and will significantly degrade the model training performance. To this end, we propose a personalized solution called TOPFL, in which only parts of the local models (not the entire models) are shared and aggregated. Moreover, considering the limited communication bandwidth on workers, we propose a topology construction algorithm to accelerate the training process. To verify the convergence of the decentralized training framework, we theoretically analyze the impact of the data heterogeneity and topology on the convergence upper bound. Extensive simulation results show that TOPFL can achieve 2.2× speedup when reaching convergence and 5.8% higher test accuracy under the same resource consumption, compared with the baseline solutions.

Federated Learning (FL) has been widely adopted to process the enormous data in the application scenarios like Edge Computing (EC). However, the commonly-used synchronous mechanism in FL may incur unacceptable waiting time for heterogeneous devices, leading to a great strain on the devices' constrained resources. In addition, the alternative asynchronous FL is known to suffer from the model staleness, which will lead to performance degradation of the trained model, especially onnon-i.i.d.data. In this paper, we design a novel asynchronous FL mechanism, named FedLC, to handle thenon-i.i.d.issue in EC by enabling the local collaboration among edge devices. Specifically, apart from uploading the local model directly to the server, each device will transmit its gradient to the other devices with different data distributions for local collaboration, which can improve the model generality. We theoretically analyze the convergence rate of FedLC and obtain the quantitative relationship between convergence bound and local collaboration. We design an efficient algorithm utilizing demand-list to determine the set of devices receiving gradients from each device. To handle the model staleness, we further assign different learning rates for various devices according to their participation frequency. The extensive experimental results demonstrate the effectiveness of our proposed mechanism.

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively train models without exposing their raw data. In most cases, the data across devices are non-independently and identically distributed in FL. Thus, the local models trained over different data distributions will inevitably deviate from the global optima, which induces optimization inconsistency and even hurts global convergence. Moreover, the resource-constrained devices with heterogeneous training capacities (e.g., computing and communication) further slow down the convergence rate. To this end, we introduce anFL framework withadaptive datasampling and localtraining, namely FAST. Specifically, even without devices’ private data distributions, FAST enables each device to sample different rates of data points from each of its local classes to rebuild a dataset for training, thus adjusting the convergence direction of the aggregated global model to be closer to the global optima. The theoretical analysis shows that the convergence bound depends on the sampling rates as well as the number of local iterations executed on the sampled data. To achieve resource-effective and convergence-guaranteed FL, we then design an online learning algorithm that jointly optimizes the data sampling and local training strategies so as to encourage the decrease of global loss under the given time budget. Extensive experiments on physical and simulated environments show that, FAST improves the model accuracy by about 1.55%-6.78% given the same time budget, and accelerates training by about 1.39-5.89× with the same target accuracy, compared with the baselines.

As some delay-sensitive mobile services such as augmented reality and autonomous driving proliferate, users' demand for low latency access to computation resources increases dramatically, and existing centralized cloud computing paradigm is difficult to solve the current dilemma. As a emerging computing paradigm in which computational capabilities are pushed from the central cloud to the network edges, Mobile Edge Computing (MEC) is expected to be an effective solution. However, due to the limited capacity (e.g. computation and bandwidth) of MEC nodes, it is not easy to maintain satisfactory quality of service for user applications. Most of the previous work is limited to reducing the processing delay by dynamically adjusting the task offloading strategy, while ignoring the key impact of access network selection on network congestion. To fill this gap, we study the joint optimization of network selection and task offloading in MEC networks with multidimensional resources constraints. To address a number of key challenges in MEC systems, including spatial demand coupling and decentralized coordination, we propose an efficient online algorithm and achieve provable close-to-optimal performance. Extensive simulation results are presented to verify the performance of our algorithm.

An SDN overlay network is a legacy network improved through SDN and overlay technology. It has some traits including the cheap upgrade cost, flexible network management and the sharing of physical network resources which has brought huge benefits to the multi-tenant cloud platform. Link failure is an important issue that shoulde be solved in any large network. In SDN overlay networks, link failure recovery brings new challenges different from the legacy network, such as how to maintain the performance of overlay networks in the post-recovery network. Thus, in the case of single link failure, we devise a recovery approach to guarantee the performance of overlay networks by the coordination between SDN switches and traditional switches. We formulate the link failure recovery (LFR) problem as an integer linear program and prove its NP-hardness. A rounding-based algorithm with bounded approximation factors is devised to solve the LFR problem. The simulation results show that the devised scheme can guarantee the performance of the overlay network after restoration. The results also show that, compared with SPR and IPFRR, the designed method can reduce the maximum link load rate by approximately 41.5% and 51.6%.