This is a corrigendum for the article “DESIGN: Online Device Selection and Edge Association for Federated Synergy Learning-enabled AIoT” published in ACM Trans. Intell. Syst. Technol. 15, 5, Article 104 (November 2024), 28 pages.

Recent advancements in federated learning have shown promising results in resource-constrained edge environments. However, with mobile devices becoming more capable of collecting data, individual client models are unable to utilize the available data due to their devices’ limited support for complex model training. Conversely, non-portable devices possess substantial computational resources, but the data they autonomously collect is insufficient to support the training of complex models. In this paper, we introduce HASFL, a novel split federated learning (SFL) framework that supports model structure heterogeneity across devices and decouples computation from the model. Through circular group training, HASFL enables mobile devices to utilize complex models to train their own data while ensuring that non-portable devices harness the data collected by mobile users. HASFL effectively addresses the challenges of applying advanced machine learning models in resource-constrained environments, leveraging the collective power of distributed devices without compromising data security. We implemented a circular group allocation method using the online algorithm to ensure cooperative training among heterogeneous models within each group while minimizing training time. In addition, we have conducted experiments to evaluate the performance of HASFL on various datasets and model architectures and analyzed the communication overhead of HASFL. The experimental results demonstrate that HASFL supports the training of heterogeneous models and significantly enhances the model’s accuracy with a relatively small increase in communication overhead.

The artificial intelligence of things (AIoT) is an emerging technology that enables numerous AIoT devices to participate in big data analytics and machine learning (ML) model training, providing various customized intelligent services for industry manufacturing. Federated learning (FL) empowers AIoT applications with privacy-preserving distributed model training without sharing raw data. However, due to IoT devices’ limited computing and memory resources, existing FL approaches for AIoT applications cannot support efficient large-scale model training. Federated synergy learning (FSyL) is a promising collaborative paradigm that alleviates the computation and communication overhead on resource-constrained AIoT devices via offloading part of the ML model to the edge server for end-to-edge collaborative training. Existing FSyL works neither efficiently address the inter-round device selection to improve model diversity nor determine the intra-round edge association to reduce the training cost, which hinders the applications of FSyL-enable AIoT. Motivated by this issue, this article first investigates the bottlenecks of executing FSyL in AIoT. It builds an optimization model of joint inter-round device selection and intra-round edge association for balancing model diversity and training cost. To tackle the intractable coupling problem, we present a framework named Online DEvice SelectIon and EdGe AssociatioN for Cost-Diversity Tradeoffs FSyL (DESIGN). First, the edge association subproblem is extracted from the original problem, and game theory determines the optimal association decision for an arbitrary device selection. Then, based on the optimal association decision, device selection is modeled as a combinatorial multi-armed bandit (CMAB) problem. Finally, we propose an online mechanism to obtain joint DESIGN decisions. The performance of DESIGN is theoretically analyzed and experimentally evaluated on real-world datasets. The results show that DESIGN can achieve up to \(84.3\%\) in cost-saving with an accuracy improvement of \(23.6\%\) compared with the state-of-the-art.

With the development of artificial intelligence technology and intelligent devices, people show great interest in intelligent applications and services, but it is impossible to complete these compute-intensive AI tasks locally, especially video analysis tasks. Edge computing is regarded as an appropriate solution to these problems. In this paper, we study the multi-user multi-server edge-end collaboration video analytics task offloading problem aiming at minimizing the overall delay for each device to finish its task. Each device chooses whether to execute the task locally or to offload the task to an edge server, and which edge server to select. At the theoretical level, we model the joint problem of task offloading and resource allocation as a mixed integer programming problem. We first determine the optimal resource allocation policy with a given task offloading decision profile. Then, task offloading problem is modeled as a congestion game and propose a decentralized mechanism to achieve a Nash equilibrium. Moreover, experimental results demonstrate that the proposed method is efficient and can significantly and steadily improve the system performance, reducing the overall delay by 33.96% on average, compared with other algorithms.

Generative adversarial imitation learning (GAIL) — a general model-free imitation learning method, allows robots to directly learn policies from expert trajectories in large environments. However, GAIL shares the limitation of other imitation learning methods that they can seldom surpass the performance of demonstrations. In this paper, to address the limit of GAIL, we propose GAN-based interactive reinforcement learning (GAIRL) from demonstrations and human evaluative feedback, by combining the advantages of GAIL and interactive reinforcement learning. We test GAIRL in six physics-based control tasks, ranging from simple low-dimensional control tasks — Cart Pole, Mountain Car and Lunar Lander, to difficult high-dimensional tasks — Inverted Double Pendulum, Hopper and HalfCheetah. Our results suggest that, the GAIRL agent can generally surpass the performance of demonstrations in both low-dimensional and high-dimensional tasks and get an optimal or close to optimal policy.

Reinforcement learning (RL) can potentially be applied to real-world robot control in complex and uncertain environments. However, it is difficult or even unpractical to design an efficient reward function for various tasks, especially those large and high-dimensional environments. Generative adversarial imitation learning (GAIL) - a general model-free imitation learning method, allows robots to directly learn policies from expert trajectories in large and high-dimensional environments. However, GAIL is still sample inefficient in terms of environmental interaction. In this paper, to solve this problem, we propose a model-based adversarial imitation learning from demonstrations and human reward (MAILDH), a novel model-based interactive imitation framework combining the advantages of GAIL, interactive RL and model-based RL. We tested our method in eight physics-based discrete and continuous control tasks for RL. Our results show that MAILDH can greatly improve the sample efficiency and robustness compared to the original GAIL.

With the explosive growth of massive data generated by smart Internet of Things (IoT) devices, federated learning has been envisioned as a promising technique to provide distributed machine learning services while protecting training data privacy. However, conventional federated learning protocols have shown significant drawbacks in regards of efficiency and scalability. First, since the synchronous communication model of federated learning and the computation capability of each device is different, the straggled users could severely desegregate the efficiency. Second, in synchronous communication, there is no effective client selection mechanism to make the model perform better in the early stage. Third, how to coordinate the communication of various nodes to accelerate global convergence is also one of the issues that need to be considered. To solve the above-mentioned problems, we propose a semi-asynchronous federated learning mechanism where a data expansion method is used to effectively reduce the stragglers existing in both synchronous and asynchronous communication models. Moreover, we also designed a priority function to make the accuracy increase rapidly in the early stage. Experimental results demonstrate that our proposed method have higher accuracy and faster convergence time compared with existing synchronization methods.