Elastically-Constrained Meta-Learner for Federated Learning

Federated learning is an approach to collaboratively training machine learning models for multiple parties that prohibit data sharing. One of the challenges in federated learning is non-IID data between clients, as a single model can not fit the data distribution for all clients. Meta-learning, such as Per-FedAvg, is introduced to cope with the challenge. Meta-learning learns shared initial parameters for all clients. Each client employs gradient descent to adapt the initialization to local data distributions quickly to realize model personalization. However, due to non-convex loss function and randomness of sampling update, meta-learning approaches have unstable goals in local adaptation for the same client. This fluctuation in different adaptation directions hinders the convergence in meta-learning. To overcome this challenge, we use the historical local adapted model to restrict the direction of the inner loop and propose an elastic-constrained method. As a result, the current round inner loop keeps historical goals and adapts to better solutions. Experiments show our method boosts meta-learning convergence and improves personalization without additional calculation and communication. Our method achieved SOTA on all metrics in three public datasets.Comment: FL-IJCAI'2

Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments

Stress arch effect on the productivity of the vertical fractured well

Rock permeability impacts by effective stress. Permeability modulus is used to evaluate the level of permeability reduction due to effective stress change. And the permeability modulus is always obtained by the experiment which assumes that the overburden pressure is constant during production. Actually, the overburden pressure reduces during production due to stress arch effect and it is easy to form a stress arch in the overburden when the reservoir is small and soft compared with surrounding’s rock. Based on the definition of the permeability modulus, we obtain an expression between permeability modulus bγ considering stress arch effect and permeability modulus b0 without stress arch. There lies a linear ship between bγ and b0, which is also proved by the experiment data. Based on the relationship between bγ and b0, a delivery equation for vertical fractured well is established. Compared with the absolute open flow with stress arch ratio of 0, the absolute open flow increases by 2.87 %, 6.79 %, 12.32 %, 20.12 % and 25.44 % for the stress arch ratio of 0.12, 0.28, 0.5, 0.8 and 1, respectively, with permeability modulus b0 of 0.0397 MPa-1. And it increases by 7.31 %, 18.1 %, 34.88 %, 61.02 % and 79.97 % for the stress arch ratio of 0.12, 0.28, 0.5, 0.8 and 1, respectively, when b0= 1. So absolute open flow with high permeability modulus b0 is more sensitive to stress arch ratio. Stress arch also impacts the optimum fracture half-length. Vertical well has the maximum absolute open flow when it has the optimum fracture half-length. The maximum absolute open flow increases with the increasing of stress arch ratio, while optimum fracture half-length decreases with increasing of stress arch ratio for the same permeability modulus b0. Compared with case with no stress arch, the optimum fracture half-length reduces by 2.86 %, 5.7 %, 11.43 %, 17.14 % and 22.86 % for the stress arch ratio of 0.12, 0.28, 0.5, 0.8 and 1 respectively when b0 equals to 0.0397 MPa-1. While the maximum absolute open flow increases by 1.6 %, 3.8 %, 7.16 %, 12.02 % and 15.60 % for the stress arch ratio of 0.12, 0.28, 0.5, 0.8 and 1 respectively. Thus, vertical well considering stress arch needs smaller fracture half-length than that with no stress arch. Meanwhile, the maximum absolute open flow and optimum fracture conductivity both increase as stress arch ratio increases. Compared with the case without stress arch, the optimum fracture conductivity increases by 50 %, while the maximum absolute open flow increases by 21.40 % with stress arch ratio of 0.5 when b0 equals to 0.0397 MPa-1. The stress arch greatly impacts on the stress sensitive permeability, permeability modulus and well performance, which can’t be neglected especially in the low and ultra-low permeability reservoir