Riyasat Ohib

In this work, we propose Salient Sparse Federated Learning (SSFL), a streamlined approach for sparse federated learning with efficient communication. SSFL identifies a sparse subnetwork prior to training, leveraging parameter saliency scores computed separately on local client data in non-IID scenarios, and then aggregated, to determine a global mask. Only the sparse model weights are communicated each round between the clients and the server. We validate SSFL’s effectiveness using standard non-IID benchmarks, noting marked improvements in the sparsity–accuracy trade-offs. Finally, we deploy our method in a real-world federated learning framework and report improvement in communication time.

Federated learning (FL) enables training of a model leveraging decentralized data in client sites while preserving privacy by not collecting data. However, one of the significant challenges of FL is limited computation and low communication bandwidth in resource limited edge client nodes. To address this several solutions have been proposed in recent times including transmitting sparse models and learning dynamic masks iteratively among others. However, many of these methods rely on transmitting the model weights throughout the training process as they are based and ad-hoc or random pruning criteria. In this work, we propose \technique which simplifies the process of sparse training by choosing a subnetwork before training based on aggregated model connection saliency scores calculated from the local client data and only sharing highly sparse gradients between server and clients during the training phase. We also demonstrate the efficacy of our method in a real world federated learning framework and report improvement in wall-clock communication time.

We design a new sparse projection method for a set of vectors that guarantees a desired average sparsity level measured leveraging the popular Hoyer measure (an affine function of the ratio of the \ell_1 and \ell_2 norms). Existing approaches either project each vector individually or require the use of a regularization parameter which implicitly maps to the average \ell_0-measure of sparsity. Instead, in our approach we set the sparsity level for the whole set explicitly and simultaneously project a group of vectors with the sparsity level of each vector tuned automatically. We show that the computational complexity of our projection operator is linear in the size of the problem. Additionally, we propose a generalization of this projection by replacing the \ell_1 norm by its weighted version. We showcase the efficacy of our approach in both supervised and unsupervised learning tasks on image datasets including CIFAR10 and ImageNet. In deep neural network pruning, the sparse models produced by our method on ResNet50 have significantly higher accuracies at corresponding sparsity values compared to existing competitors. In nonnegative matrix factorization, our approach yields competitive reconstruction errors against state-of-the-art algorithms.

Deep Reinforcement Learning (RL) is a powerful framework for solving complex real-world problems. Large neural networks employed in the framework are traditionally associated with better generalization capabilities, but their increased size entails the drawbacks of extensive training duration, substantial hardware resources, and longer inference times. One way to tackle this problem is to prune neural networks leaving only the necessary parameters. State-of-the-art concurrent pruning techniques for imposing sparsity perform demonstrably well in applications where data-distributions are fixed. However, they have not yet been substantially explored in the context of RL. We close the gap between RL and single-shot pruning techniques and present a general pruning approach to the Offline RL. We leverage a fixed dataset to prune neural networks before the start of RL training. We then run experiments varying the network sparsity level and evaluating the validity of pruning at initialization techniques in continuous control tasks. Our results show that with 95% of the network weights pruned, Offline-RL algorithms can still retain performance in the majority of our experiments. To the best of our knowledge no prior work utilizing pruning in RL retained performance at such high levels of sparsity. Moreover, pruning at initialization techniques can be easily integrated into any existing Offline-RL algorithms without changing the learning objective.

Accumulating empirical evidence shows that very large deep learning models learn faster and achieve higher accuracy than their smaller counterparts. Yet, smaller models have benefits of energy efficiency and are often easier to interpret. To simultaneously get the benefits of large and small models we often encourage sparsity in the model weights of large models. For this, different approaches have been proposed including weight-pruning and distillation. Unfortunately, most existing approaches do not have a controllable way to request a desired value of sparsity as an interpretable parameter and get it right in a single run. In this work, we design a new sparse projection method for a set of weights in order to achieve a desired average level of sparsity without additional hyperparameter tuning which is measured using the ratio of the l1 and l2 norms. Instead of projecting each vector of the weight matrix individually, or using sparsity as a regularizer, we project all vectors together to achieve an average target sparsity, where the sparsity levels of the individual vectors of the weight matrix are automatically tuned. Our projection operator has the following guarantees – (A) it is fast and enjoys a runtime linear in the size of the vectors; (B) the solution is unique except for a measure set of zero. We utilize our projection operator to obtain the desired sparsity of deep learning models in a single run with a negligible performance hit, while competing methods require sparsity hyperparameter tuning. Even with a single projection of a pre-trained dense model followed by fine-tuning, we show empirical performance competitive to the state of the art. We support these claims with empirical evidence on real-world datasets and on a number of architectures, comparing it to other state of the art methods including DeepHoyer.