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One-shot federated learning (OSFL) has gained attention in recent years due to its low communication cost. However, most of the existing methods require auxiliary datasets or training generators, which hinders their practicality in real-world scenarios. In this paper, we explore the novel opportunities that diffusion models bring to OSFL and propose FedCADO, utilizing guidance from client classifiers to generate data that complies with clients' distributions and subsequently training the aggregated model on the server. Specifically, our method involves targeted optimizations in two aspects. On one hand, we conditionally edit the randomly sampled initial noises, embedding them with specified semantics and distributions, resulting in a significant improvement in both the quality and stability of generation. On the other hand, we employ the BN statistics from the classifiers to provide detailed guidance during generation. These tailored optimizations enable us to limitlessly generate datasets, which closely resemble the distribution and quality of the original client dataset. Our method effectively handles the heterogeneous client models and the problems of non-IID features or labels. In terms of privacy protection, our method avoids training any generator or transferring any auxiliary information on clients, eliminating any additional privacy leakage risks. Leveraging the extensive knowledge stored in the pre-trained diffusion model, the synthetic datasets can assist us in surpassing the knowledge limitations of the client samples, resulting in aggregation models that even outperform the performance ceiling of centralized training in some cases, which is convincingly demonstrated in the sufficient quantification and visualization experiments conducted on three large-scale multi-domain image datasets.

Offline reinforcement learning suffers from the out-of-distribution issue and extrapolation error. Most policy constraint methods regularize the density of the trained policy towards the behavior policy, which is too restrictive in most cases. We propose Supported Trust Region optimization (STR) which performs trust region policy optimization with the policy constrained within the support of the behavior policy, enjoying the less restrictive support constraint. We show that, when assuming no approximation and sampling error, STR guarantees strict policy improvement until convergence to the optimal support-constrained policy in the dataset. Further with both errors incorporated, STR still guarantees safe policy improvement for each step. Empirical results validate the theory of STR and demonstrate its state-of-the-art performance on MuJoCo locomotion domains and much more challenging AntMaze domains.

Federated learning (FL) aims to collaboratively train a shared model across multiple clients without transmitting their local data. Data heterogeneity is a critical challenge in realistic FL settings, as it causes significant performance deterioration due to discrepancies in optimization among local models. In this work, we focus on label distribution skew, a common scenario in data heterogeneity, where the data label categories are imbalanced on each client. To address this issue, we propose FedBalance, which corrects the optimization bias among local models by calibrating their logits. Specifically, we introduce an extra private weak learner on the client side, which forms an ensemble model with the local model. By fusing the logits of the two models, the private weak learner can capture the variance of different data, regardless of their category. Therefore, the optimization direction of local models can be improved by increasing the penalty for misclassifying minority classes and reducing the attention to majority classes, resulting in a better global model. Extensive experiments show that our method can gain 13\% higher average accuracy compared with state-of-the-art methods.

Machine learning models are being increasingly deployed to take, or assist in taking, complicated and high-impact decisions, from quasi-autonomous vehicles to clinical decision support systems. This poses challenges, particularly when models have hard-to-detect failure modes and are able to take actions without oversight. In order to handle this challenge, we propose a method for a collaborative system that remains safe by having a human ultimately making decisions, while giving the model the best opportunity to convince and debate them with interpretable explanations. However, the most helpful explanation varies among individuals and may be inconsistent across stated preferences. To this end we develop an algorithm, Ardent, to efficiently learn a ranking through interaction and best assist humans complete a task. By utilising a collaborative approach, we can ensure safety and improve performance while addressing transparency and accountability concerns. Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations, which we validate through extensive simulations alongside a user study involving a challenging image classification task, demonstrating consistent improvement over competing systems.

Personalized federated learning (PFL) is an approach proposed to address the issue of poor convergence on heterogeneous data. However, most existing PFL frameworks require strong assumptions for convergence. In this paper, we propose an alternating direction method of multipliers (ADMM) for training PFL models with Moreau envelope (FLAME), which achieves a sublinear convergence rate, relying on the relatively weak assumption of gradient Lipschitz continuity. Moreover, due to the gradient-free nature of ADMM, FLAME alleviates the need for hyperparameter tuning, particularly in avoiding the adjustment of the learning rate when training the global model. In addition, we propose a biased client selection strategy to expedite the convergence of training of PFL models. Our theoretical analysis establishes the global convergence under both unbiased and biased client selection strategies. Our experiments validate that FLAME, when trained on heterogeneous data, outperforms state-of-the-art methods in terms of model performance. Regarding communication efficiency, it exhibits an average speedup of 3.75x compared to the baselines. Furthermore, experimental results validate that the biased client selection strategy speeds up the convergence of both personalized and global models.

Decentralized learning is widely employed for collaboratively training models using distributed data over wireless networks. Existing decentralized learning methods primarily focus on training single-modal networks. For the decentralized multi-modal learning (DMML), the modality heterogeneity and the non-independent and non-identically distributed (non-IID) data across devices make it difficult for the training model to capture the correlated features across different modalities. Moreover, modality competition can result in training imbalance among different modalities, which can significantly impact the performance of DMML. To improve the training performance in the presence of non-IID data and modality heterogeneity, we propose a novel DMML with knowledge distillation (DMML-KD) framework, which decomposes the extracted feature into the modality-common and the modality-specific components. In the proposed DMML-KD, a generator is applied to learn the global conditional distribution of the modality-common features, thereby guiding the modality-common features of different devices towards the same distribution. Meanwhile, we propose to decrease the number of local iterations for the modalities with fast training speed in DMML-KD to address the imbalanced training. We design a balance metric based on the parameter variation to evaluate the training speed of different modalities in DMML-KD. Using this metric, we optimize the number of local iterations for different modalities on each device under the constraint of remaining energy on devices. Experimental results demonstrate that the proposed DMML-KD with training balance can effectively improve the training performance of DMML.

We consider the problem of learning the exact skeleton of general discrete Bayesian networks from potentially corrupted data. Building on distributionally robust optimization and a regression approach, we propose to optimize the most adverse risk over a family of distributions within bounded Wasserstein distance or KL divergence to the empirical distribution. The worst-case risk accounts for the effect of outliers. The proposed approach applies for general categorical random variables without assuming faithfulness, an ordinal relationship or a specific form of conditional distribution. We present efficient algorithms and show the proposed methods are closely related to the standard regularized regression approach. Under mild assumptions, we derive non-asymptotic guarantees for successful structure learning with logarithmic sample complexities for bounded-degree graphs. Numerical study on synthetic and real datasets validates the effectiveness of our method. Code is available at //github.com/DanielLeee/drslbn.

Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address privacy concerns, FL systems need to safeguard the clients' data from disclosure during training and control data leakage through trained models when exposed to untrusted domains. Distributed differential privacy (DP) offers an appealing solution in this regard as it achieves a balanced tradeoff between privacy and utility without a trusted server. However, existing distributed DP mechanisms are impractical in the presence of client dropout, resulting in poor privacy guarantees or degraded training accuracy. In addition, these mechanisms suffer from severe efficiency issues. We present Dordis, a distributed differentially private FL framework that is highly efficient and resilient to client dropout. Specifically, we develop a novel `add-then-remove' scheme that enforces a required noise level precisely in each training round, even if some sampled clients drop out. This ensures that the privacy budget is utilized prudently, despite unpredictable client dynamics. To boost performance, Dordis operates as a distributed parallel architecture via encapsulating the communication and computation operations into stages. It automatically divides the global model aggregation into several chunk-aggregation tasks and pipelines them for optimal speedup. Large-scale deployment evaluations demonstrate that Dordis efficiently handles client dropout in various realistic FL scenarios, achieving the optimal privacy-utility tradeoff and accelerating training by up to 2.4$\times$ compared to existing solutions.

To date, most existing self-supervised learning methods are designed and optimized for image classification. These pre-trained models can be sub-optimal for dense prediction tasks due to the discrepancy between image-level prediction and pixel-level prediction. To fill this gap, we aim to design an effective, dense self-supervised learning method that directly works at the level of pixels (or local features) by taking into account the correspondence between local features. We present dense contrastive learning, which implements self-supervised learning by optimizing a pairwise contrastive (dis)similarity loss at the pixel level between two views of input images. Compared to the baseline method MoCo-v2, our method introduces negligible computation overhead (only <1% slower), but demonstrates consistently superior performance when transferring to downstream dense prediction tasks including object detection, semantic segmentation and instance segmentation; and outperforms the state-of-the-art methods by a large margin. Specifically, over the strong MoCo-v2 baseline, our method achieves significant improvements of 2.0% AP on PASCAL VOC object detection, 1.1% AP on COCO object detection, 0.9% AP on COCO instance segmentation, 3.0% mIoU on PASCAL VOC semantic segmentation and 1.8% mIoU on Cityscapes semantic segmentation. Code is available at: //git.io/AdelaiDet

Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks. Despite recent progress, efficient exploration in meta-RL remains a key challenge in sparse-reward tasks, as it requires quickly finding informative task-relevant experiences in both meta-training and adaptation. To address this challenge, we explicitly model an exploration policy learning problem for meta-RL, which is separated from exploitation policy learning, and introduce a novel empowerment-driven exploration objective, which aims to maximize information gain for task identification. We derive a corresponding intrinsic reward and develop a new off-policy meta-RL framework, which efficiently learns separate context-aware exploration and exploitation policies by sharing the knowledge of task inference. Experimental evaluation shows that our meta-RL method significantly outperforms state-of-the-art baselines on various sparse-reward MuJoCo locomotion tasks and more complex sparse-reward Meta-World tasks.