Nowadays, federated learning has been a prevailing paradigm for large-scale distributed machine learning, which is faced with the problem of communication bottleneck. To solve this problem, recent works usually apply different compression techniques such as sparsification and quantization compressors. However, such approaches are all lossy compression and have two drawbacks. First, they could lead to information loss of the global parameter. Second, compressed parameters carrying less information would be more likely to be attacked by malicious workers than full parameters, leading to a Byzantine failure of the model. In this paper, to avoid information loss, mitigate the communication bottleneck, and at the same time tolerate popular Byzantine attacks, we propose FedGraD, which leverages gradient difference compression and combines robust aggregation rules in federated learning settings. Our experimental results on three different datasets a9a, w8a and mushrooms show good performance of our method.

Nowadays, as a prevailing paradigm for large-scale machine learning, distributed learning has been faced with two challenges, communication bottleneck and limited robustness. For the communication challenge, compression is widely-used as a solution. For the robustness challenge, some robust defence methods have been proposed. However, previous works that simultaneously consider these two challenges are limited. Through an experiment on the w8a dataset, we found that compressed distributed learning with rand-K is vulnerable to poisoning attacks. Therefore, in this paper, we propose a robust compressed distributed framework for distributed learning settings. Experimental evaluations on a9a and w8a datasets have shown the effectiveness of our proposed framework, which markedly decreases the average optimality gap from 1.47E −2 and 2.15E −2 to 3.98E −4 and 4.33E −4 respectively.

Uncertainty estimation plays an important role for future reliable deployment of deep segmentation models in safety-critical scenarios such as medical applications. However, existing methods for uncertainty estimation have been limited by the lack of explicit guidance for calibrating the prediction risk and model confidence. In this work, we propose a novel fine-grained reward maximization (FGRM) framework, to address uncertainty estimation by directly utilizing an uncertainty metric related reward function with a reinforcement learning based model tuning algorithm. This would benefit the model uncertainty estimation with direct optimization guidance for model calibration. Specifically, our method designs a new uncertainty estimation reward function using the calibration metric, which is maximized to fine-tune an evidential learning pre-trained segmentation model for calibrating prediction risk. Importantly, we innovate an effective fine-grained parameter update scheme, which imposes fine-grained reward-weighting of each network parameter according to the parameter importance quantified by the fisher information matrix. To the best of our knowledge, this is the first work exploring reward optimization for model uncertainty estimation in safety-critical vision tasks. The effectiveness of our method is demonstrated on two large safety-critical surgical scene segmentation datasets under two different uncertainty estimation settings. With real-time one forward pass at inference, our method outperforms state-of-the-art methods by a clear margin on all the calibration metrics of uncertainty estimation, while maintaining a high task accuracy for the segmentation results. Code is available at https://github.com/med-air/FGRM.