Federated learning (FL) offers a decentralized approach to machine learning. In FL, models are trained by the data from multiple devices or clients without necessarily centralizing this data, thus preserving privacy and reducing the need for data transfer. Despite its potential, FL faces inherent obstacles, most notably the challenge of achieving a fast convergence rate, especially with large, non-identically distributed client datasets. Also, weight pruning, an effective approach to reduce the number of weight parameters in a deep neural network, is hard to be employed on FL because it involves additional challenges to the convergence between different clients. To deal with the above issues, we propose a unified framework for the convergence and weight pruning in FL. We leverage the inherent structure of the Alternating Direction Method of Multipliers (ADMM) to partition the primary loss function for individual clients and apply specific dual variables to hasten the global model’s convergence. Our method, when tested on MNIST and SVHN datasets, consistently outperforms the established approaches on the convergence rate and model accuracy under the same weight pruning rate. For example, when the ResNet-18 model is pruned by 100 ×, our method achieves 0.65% to 3.09% accuracy improvement for the SVHN dataset under federated learning with non-identically distributed (non-IID) data distribution compared with the established approaches.

Federated Multi-Task Learning (FMTL) enables collaborative training of multiple tasks across decentralized clients, but faces two key challenges in practice: negative transfer among tasks and scalability under resource constraints. Task differences can cause gradient conflicts that degrade overall performance, while limited computation and storage on edge devices make it difficult to maintain accuracy with low overhead. Existing methods address these issues either by adopting multi-backbone architectures, which split tasks to reduce interference but incur substantial parameter and computation costs, or by performing naive global averaging, which ignores inter-task differences and fails to effectively mitigate negative transfer. To overcome these limitations, we propose Task-Aware Federated Multi-Task Learning (TA-FMTL), a single-backbone framework that balances accuracy and efficiency. TA-FMTL integrates two lightweight components: a min–max task-difficulty weighting strategy that dynamically allocates more updates to harder tasks for balanced optimization, and a variance-aware reputation aggregation that down-weights clients with high overall loss or unstable cross-task performance. This design enables robust coordination across heterogeneous tasks without task splitting. Experiments on the Taskonomy benchmark show that TA-FMTL consistently achieves better or comparable accuracy to state-of-the-art MAS variants while reducing parameters by up to 77.2% and FLOPs by 37.3% in challenging 5-task and 9-task settings, demonstrating its scalability and practicality for real-world FMTL under heterogeneous and resource-limited conditions.

As deep neural networks (DNNs) achieve extraordinary performance in a wide range of tasks, testing their robustness under adversarial attacks becomes paramount. Adversarial attacks, also known as adversarial examples, are used to measure the robustness of DNNs and are generated by incorporating imperceptible perturbations into the input data with the intention of altering a DNN's classification. In prior work in this area, most of the proposed optimization based methods employ gradient descent to find adversarial examples. In this paper, we present an innovative method which generates adversarial examples via convex programming. Our experiment results demonstrate that we can generate adversarial examples with lower distortion and higher transferability than the C&W attack, which is the current state-of-the-art adversarial attack method for DNNs. We achieve 100% attack success rate on both the original undefended models and the adversarially-trained models. Our distortions of the L_inf attack are respectively 31% and 18% lower than the C&W attack for the best case and average case on the CIFAR-10 data set.