In this paper, we introduce a layer-wise compensated aggregation algorithm designed for federated learning in dynamic environments. To reduce data transmission and maintain model continuity, our method implements a hierarchical update strategy that avoids artificially modular partition of neural networks and thus more flexible in practice. Specifically, weight layers closer to the output, which significantly impact model performance, receive direct updates. Conversely, layers further from the output with minimal influence, are updated using gradient compensation from the previous iteration. To ensure consistency across the model, we compensate these less influential weight layers with residual values from earlier rounds, thus enhancing integration and continuity. Experimental results from two datasets confirm that our algorithm not only secures stable convergence but also matches the generalization performance of traditional federated averaging, even under conditions with dropout rates as high as 90%.

In this article, we propose ABR-FL, an adaptive recovery and selective backup algorithm designed to manage models compromised in federated learning (FL), while minimizing computational costs for clients and memory costs for the server. Our algorithm integrates multiple stages, including selective backup mechanisms, recovery point selection, initial recovery stage fine-tuning, approximation training using historical gradients, and post-approximation fine-tuning. Initially, the server evaluates client updates using a Taylor expansion, backing up only those updates that align well with the global model and excluding potentially malicious updates. To optimize memory usage and enhance computational efficiency, the server evaluates updates over the most recent k iterations, groups clients, and assesses updates from each cluster. Subsequently, the server performs a sensitivity analysis on historical backup models and stores models with sensitivity below a certain threshold in a starting point pool for potential recovery initiation. The recovery stage involves fine-tuning the model to comprehend the model’ s learning trajectory and ensure that the approximation training process on the server aligns with this trajectory. The server employs second-order gradient information (Hessian approximations) and a rank-two correction matrix to calculate the recovered gradient from the historical gradient. Post-approximation fine-tuning addresses errors stemming from omitted higher order terms by making small, precise adjustments to the model parameters. Theoretically, we establish that the global model recovered by ABR-FL closely approximates that of a model trained from scratch under certain assumptions. Finally, we build a FL system and conduct extensive experiments to demonstrate the effectiveness of our algorithm in terms of model accuracy.

Multicast short video streaming can enhance bandwidth utilization by enabling simultaneous video transmission to multiple users over shared wireless channels. The existing network management schemes mainly rely on the sequential buffering principle and general quality of experience (QoE) model, which may deteriorate QoE when users’ swipe behaviors exhibit distinct spatiotemporal variation. In this paper, we propose a digital twin (DT)-based network management scheme to enhance QoE. Firstly, user status emulated by the DT is utilized to estimate the transmission capabilities and watching probability distributions of sub-multicast groups (SMGs) for an adaptive segment buffering. The SMGs’ buffers are aligned to the unique virtual buffers managed by the DT for a fine-grained buffer update. Then, a multicast QoE model consisting of rebuffering time, video quality, and quality variation is developed, by considering the mutual influence of segment buffering among SMGs. Finally, a joint optimization problem of segment version selection and slot division is formulated to maximize QoE. To efficiently solve the problem, a data-model-driven algorithm is proposed by integrating a convex optimization method and a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed DT-based network management scheme outperforms benchmark schemes in terms of QoE improvement.

In this paper, we propose an adaptive distributed learning algorithm that not only resists three types of Byzantine attacks (i.e., gradient negative direction attacks, gradient partial dimension zeroing attacks, gradient scaling attacks) but also ensures high model accuracy. The proposed algorithm is built on a fully distributed model: clients share their local model updates with a group of dynamic committee clients, who cooperatively and iteratively train a global model. Specifically, to counter gradient negative direction attacks, we design a method based on gradient projection that maps clients' local gradients into small subspaces. The design allows committee clients to efficiently and precisely filter out adversarial clients by comparing angles between these subspaces. Moreover, considering that data heterogeneity among clients may cause misdetections of gradient partial dimension zeroing and scaling attacks, thereby reducing model accuracy, we introduce an adaptive multi-dimensional scoring method, which is applied after the gradient-projection-based filtering. The method assists committee clients in scoring and selecting most suitable clients for model aggregation using three hyperparameters, and thus achieves a balance between model accuracy and security. Finally, we conduct extensive experiments on real-world datasets to show the proposed algorithm's effectiveness: it can achieve Byzantine robustness and simultaneously maintain high model accuracy.

Distributed learning has been widely adopted to train a global model from local data. However, its performance can be severely affected by stragglers. Recently, some research has been dedicated to resolving the straggler problem by adopting gradient coding, the essence of gradient coding is to solve the straggler problem by adding data redundancy. However, the large amount of data redundancy as well as computation and communication overhead that it brings is still hard to be resolved. Besides, the complexity of the encoding and decoding will increase linearly with the number of the local workers. To this end, in this paper, we design a lightweight coding method in the computing phase and seek to ensure fair transmission in the communication phase. Specifically, to tolerate stragglers in computing phase, we propose a two-stage dynamic coding scheme, part of the workers start computing the partial gradients from the data partitions assigned in the first stage, and the remaining workers for computation in the second stage is decided based on which workers have finished in the first stage. To further tolerate stragglers in the communication phase, a perturbed Lyapunov function is designed to maximize admission data balancing fairness as well as the throughput. The experimental result verifies the derived properties and demonstrates that our proposed solution can achieve a better performance for practical network parameters and benchmark data in terms of accuracy and resource utilization in the distributed learning system.

We investigate a cooperative federated learning framework among devices for mobile edge computing,named (CFLMEC), where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC,devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.

Design an optimization model for task management among Mobile Terminal (MT), Macro cell Base Station (MBS), and multiple Small cell Base Stations (SBS) for the large-scale Mobile Edge Computing (MEC) system, is a challenging issue due to the large number of tasks and SBSs. Inspired by this, we propose a Parallel Optimal Task Allocation Mechanism (POTAM) framework for MEC, which includes Device to Device (D2D)-enabled computing, MBS computing and Edge Computation Resource Distribution (ECRD) computing. In POTAM, we exploit a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of delay and energy consumptions, which formulates the task allocation under these requirements as a nonlinear 0–1 integer programming problem. To solve this problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing and Cyclic Block coordinate Gradient Projection (CBGP) methods, which can guarantee convergence and reduce computational complexity with a good scalability. In order to allocate task cooperatively, an optimal approach is proposed, ECRD-A, which is used to find the shortest path among each node. Numerical results demonstrate the effectiveness of the POTAM and it can effectively reduce delay and energy consumption for a large-scale MEC system.

We consider the problem of the intelligent and efficient resource management framework in mobile-edge computing (MEC), which can reduce delay and energy consumption, and features distributed optimization and efficient congestion avoidance. In this article, we present a cooperative learning framework for resource management in MEC from an alternating direction method of multipliers (ADMMs) perspective, named the CL-ADMM framework. First, computing a task requires both the user personal data and corresponding program that processes it, to efficiently cache program in a group, a novel program popularity estimation scheme is proposed, which is based on a semi-Markov process model. Then, a greedy program cooperative caching mechanism is established, which can effectively reduce delay and energy consumption. Second, to address group congestion, a dynamic task migration scheme based on improved cooperative Q-learning is proposed, which can effectively reduce delay and alleviate congestion. Third, to minimize delay and energy consumption for resource allocation in a group, we formulate it as an optimization problem with a large number of variables, and then exploit a novel ADMM-based scheme to solve this problem, which can reduce the complexity of the problem with a new set of auxiliary variables, these subproblems are all convex problems that can be solved by using a primal-dual approach, which guarantees its convergence. Finally, we prove its convergence by using the Lyapunov theory. The numerical results demonstrate the effectiveness of the CL-ADMM framework in reducing delay and energy consumption in MEC.

We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parallel and distributed optimization. In this paper, we study the joint optimization model to consider cooperative task management mechanism among mobile terminals (MT), macro cell base station (MBS), and multiple small cell base station (SBS) for large-scale MEC applications. We propose a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of low delay and low energy consumption in the MEC system which formulates the task allocation under those requirements as a nonlinear 0-1 integer programming problem. To solve the optimization problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing (for global variables updating) and the Cyclic Block coordinate Gradient Projection (CBGP, for local variables updating) methods, which can guarantee convergence and reduce computational complexity with a good scalability. Numerical results demonstrate the effectiveness of the proposed mechanism and it can effectively reduce delay and energy consumption for a large-scale MEC system.

Recently, the increase of different services makes the design of routing protocols more difficult in mobile ad hoc networks (MANETs), e.g., how to guarantee the QoS of different types of traffics flows in MANETs with resource constrained and malicious nodes. opportunistic routing (OR) can make full use of the broadcast characteristics of wireless channels to improve the performance of MANETs. In this paper, we propose a traffic-differentiated secure opportunistic routing from a game theoretic perspective, DSOR. In the proposed scheme, we use a novel method to calculate trust value, considering node's forwarding capability and the status of different types of flows. According to the resource status of the network, we propose a service price and resource price for the auction model, which is used to select optimal candidate forwarding sets. At the same time, the optimal bid price has been proved and a novel flow priority decision for transmission is presented, which is based on waiting time and requested time. The simulation results show that the network lifetime, packet delivery rate and delay of the DSOR are better than existing works.