FedCode: Communication-Efficient Federated Learning via Transferring Codebooks

Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized local data. While FL offers appealing properties for clients' data privacy, it imposes high communication burdens for exchanging model weights between a server and the clients. Existing approaches rely on model compression techniques, such as pruning and weight clustering to tackle this. However, transmitting the entire set of weight updates at each federated round, even in a compressed format, limits the potential for a substantial reduction in communication volume. We propose FedCode where clients transmit only codebooks, i.e., the cluster centers of updated model weight values. To ensure a smooth learning curve and proper calibration of clusters between the server and the clients, FedCode periodically transfers model weights after multiple rounds of solely communicating codebooks. This results in a significant reduction in communication volume between clients and the server in both directions, without imposing significant computational overhead on the clients or leading to major performance degradation of the models. We evaluate the effectiveness of FedCode using various publicly available datasets with ResNet-20 and MobileNet backbone model architectures. Our evaluations demonstrate a 12.2-fold data transmission reduction on average while maintaining a comparable model performance with an average accuracy loss of 1.3% compared to FedAvg. Further validation of FedCode performance under non-IID data distributions showcased an average accuracy loss of 2.0% compared to FedAvg while achieving approximately a 12.7-fold data transmission reduction.

PCB Defect Detection Using Denoising Convolutional Autoencoders

Printed Circuit boards (PCBs) are one of the most important stages in making electronic products. A small defect in PCBs can cause significant flaws in the final product. Hence, detecting all defects in PCBs and locating them is essential. In this paper, we propose an approach based on denoising convolutional autoencoders for detecting defective PCBs and to locate the defects. Denoising autoencoders take a corrupted image and try to recover the intact image. We trained our model with defective PCBs and forced it to repair the defective parts. Our model not only detects all kinds of defects and locates them, but it can also repair them as well. By subtracting the repaired output from the input, the defective parts are located. The experimental results indicate that our model detects the defective PCBs with high accuracy (97.5%) compare to state of the art works.