Trustworthy multi-modal learning reliably integrates multiple data sources. However, current methods often face a challenge, which is the inherent non-convex nature of deep neural networks. It leads to their susceptibility to local minima, ultimately resulting in a reduced capacity for generalization. To address this issue, we first create a theoretical framework, which extends the application of curriculum learning in multi-modal scenarios. Secondly, we propose a novel curriculum termed the Dynamic SRM Curriculum (DSRMC). It consists of two modules: a scoring function and a training schedule. The scoring function sorts samples from simple to complex. The training scheduler aims to manage the quantity of samples supplied at each round during training. DSRMC facilitates positioning the learned model in a flatter region of the loss landscape, thereby enhancing its overall generalization ability. Building on DSRMC, we eventually propose an innovative method termed as Curriculum Trustworthy Multi-modal Learning (CTML). It applies DSRMC in multi-modal learning application scenarios. Extensive experiments conducted on three open datasets show that the proposed CTML outperforms state-of-the-art methods, with a maximum improvement of 6.7% in macro F1 score. Our code and dataset are publicly available on https://github.com/HackerHyper/DSRMC.git .

Federated learning (FL) enables collaborative model training across distributed edge devices without sharing raw data, but the large number of devices and frequent parameter exchanges create substantial communication overhead. To improve communication efficiency, event-triggered mechanisms have been introduced. However, such approaches may exclude valuable but inactive users (i.e., IoT devices with informative data but limited activity) or overlook subtle but important updates, resulting in suboptimal trade-offs between communication efficiency and training performance. To address this, we propose Stochastic Event-Triggered Lazy Aggregation with Stochastic Quantization (SET-LASQ). SET-LASQ employs a probabilistic triggering strategy that adaptively balances the participation of highly contributing users with the reactivation of long-inactive ones, while incorporating gradient quantization to further reduce transmitted bits. Under strong convexity, we theoretically prove that SET-LASQ achieves sublinear convergence with a diminishing stepsize and linear convergence to a steady-state neighborhood with a fixed stepsize. Extensive experiments on benchmark datasets verify that SET-LASQ reduces both communication rounds and transmission volume while maintaining competitive model accuracy, consistently outperforming deterministic event-triggered and lazy aggregation baselines under independent and non-independent data distributions.

Trustworthy multi-modal learning integrates multiple sources of data reliably. However, the current methods still focus on performance improvement by developing deep multi-modal networks. These approaches frequently encounter challenges due to the inherent non-convex nature of deep neural networks and their vulnerability to local minima, ultimately leading to a diminished ability for generalization. To address this problem, we present a novel curriculum termed the Dynamic SRM Curriculum (DSRMC). Within DSRMC, the deep trustworthy multi-modal networks undergo training with data provided sequentially, progressing from simple to complex samples. This training strategy mimics the human learning process, commencing with fundamental concepts and gradually advancing to tackle more complex and abstract ideas. Building upon DSRMC, we propose an innovative Curriculum Trustworthy Multi-modal Learning (CTML) method. CTML makes it easier to place the learned model in a flatter area, which improves its overall ability for generalization. Comprehensive experiments on three public datasets demonstrate that the proposed CTML performs better than state-of-the-art methods, achieving a maximum improvement of 6.7% on macroF1.

This article investigates the online operation of distributed data centers equipped with energy battery. We aim to minimize their long-term operational cost by optimally distributing workload among data centers and operating energy battery. However, future spatio-temporally variant uncertainties in both workload and electricity prices have been the main impediment for a performance-guaranteed online data center operation strategy. To address this issue, we develop a fully distributed online algorithm that decouples workload distribution and battery operation across the network and time by introducing well-designed virtual queues for workload and batteries into the framework of Lyapunov optimization. Theoretically, an analytical gap between the long-term operational cost achieved by our algorithm and the theoretical optimum is provided to corroborate the desirable operation strategy. Extensive simulations using the real-world workload and electricity price data demonstrate the cost-delay tradeoff that our algorithm strikes and validate the theoretical results that we obtained.

This paper focuses on communication-efficient federated learning problem, and develops a novel distributed quantized gradient approach, which is characterized by adaptive communications of the quantized gradients. Specifically, the federated learning builds upon the server-worker infrastructure, where the workers calculate local gradients and upload them to the server; then the server obtain the global gradient by aggregating all the local gradients and utilizes it to update the model parameter. The key idea to save communications from the worker to the server is to quantize gradients as well as skip less informative quantized gradient communications by reusing previous gradients. Quantizing and skipping result in 'lazy' worker-server communications, which justifies the term Lazily Aggregated Quantized (LAQ) gradient. Theoretically, the LAQ algorithm achieves the same linear convergence as the gradient descent in the strongly convex case, while effecting major savings in the communication in terms of transmitted bits and communication rounds. Empirically, extensive experiments using realistic data corroborate a significant communication reduction compared with state-of-the-art gradient- and stochastic gradient-based algorithms.

The huge amount of energy consumed by the data centers around the world every year motivates the cloud service providers to operate data centers in a more energy efficient way. A promising solution is to turn off the idle servers, which, however, may be turned on later, incurring a significant startup cost. The problem turns to dynamically provisioning the workload, and cutting down the energy cost which includes the power to support the running of data center and the startup cost. Different from previous studies that usually consider the worst-case performance guarantee when designing online algorithms, this paper considers the average case which is more practical. We propose a simple online algorithm based on the expectation of job interval of workload, which is proven to be optimal for exponential and uniform distributions and achieves tight competitive ratio$\frac{e}{e-1}$and$\frac{4}{3}$, respectively, for them. Simulations using the synthetic data verify our theoretical analysis. Numerical results employing Google’s data center workload trace demonstrate that the proposed algorithm outperforms the worst case-based algorithm in terms of operation cost reduction.

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice. The goal of the group of agents is to collaboratively learn the value function of a given policy from locally private rewards observed in a shared environment, through exchanging local estimates with neighbors. Despite their simplicity and widespread use, our theoretical understanding of such decentralized TD learning algorithms remains limited. Existing results were obtained based on i.i.d. data samples, or by imposing an ‘additional’ projection step to control the ‘gradient’ bias incurred by the Markovian observations. In this paper, we provide a finite-time analysis of the fully decentralized TD(0) learning under both i.i.d. as well as Markovian samples, and prove that all local estimates converge linearly to a small neighborhood of the optimum. The resultant error bounds are the first of its type—in the sense that they hold under the most practical assumptions—which is made possible by means of a novel multi-step Lyapunov approach.

The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication. The key idea is to first quantize the computed gradients, and then skip less informative quantized gradient communications by reusing outdated gradients. Quantizing and skipping result in 'lazy' worker-server communications, which justifies the term Lazily Aggregated Quantized gradient that is henceforth abbreviated as LAQ. Our LAQ can provably attain the same linear convergence rate as the gradient descent in the strongly convex case, while effecting major savings in the communication overhead both in transmitted bits as well as in communication rounds. Empirically, experiments with real data corroborate a significant communication reduction compared to existing gradient- and stochastic gradient-based algorithms.