Federated learning has emerged as a promising approach to enable edge devices to collaboratively learn a shared model while keeping the training data on device, thereby decoupling the ability to do model training from the need to store the data in the cloud. In this regard, Federated matched averaging (FedMA) algorithm has been proposed for federated learning of modern neural network architectures such as convolutional neural networks (CNNs) and LSTMs. FedMA constructs the shared global model in a layer-wise manner by matching and averaging hidden elements with similar feature extraction signatures. Specifically, it matches channels for convolution layers, hidden states for LSTM, and neurons for fully connected layers. The performance of FedMA has been evaluated through experiments on deep CNN and LSTM architectures trained on real-world datasets. The results indicate that FedMA not only outperforms popular state-of-the-art federated learning algorithms but also reduces overall communication burden. For instance, Table 1 shows that FedMA achieves a final accuracy of 87.53% for VGG-9 trained on CIFAR-10 dataset compared to 86.29% achieved by FedAvg and 85.32% achieved by FedProx Ensemble methods. Addressing data bias is an important aspect of machine learning models since real-world data often exhibit multimodality within each class leading to biases in classification models trained on such data. To address this issue, various techniques have been proposed such as inclusive images (Doshi 2018). In conclusion, Federated matched averaging algorithm offers a promising solution for federated learning of modern neural network architectures while reducing communication burden and achieving better performance than existing state-of-the-art methods. Furthermore, addressing data bias is essential for improving the generalizability of machine learning models trained on real-world datasets.
- Federated learning enables edge devices to collaboratively learn a shared model while keeping the training data on device, decoupling the ability to do model training from the need to store data in the cloud.
- Federated matched averaging (FedMA) algorithm has been proposed for federated learning of modern neural network architectures such as convolutional neural networks (CNNs) and LSTMs.
- FedMA constructs the shared global model in a layer-wise manner by matching and averaging hidden elements with similar feature extraction signatures.
- FedMA outperforms popular state-of-the-art federated learning algorithms and reduces overall communication burden.
- Addressing data bias is important for improving generalizability of machine learning models trained on real-world datasets.
- Various techniques have been proposed such as inclusive images (Doshi 2018).
Federated learning is when devices work together to learn something without sharing their private information. FedMA is a special way of doing federated learning for complicated things like pictures and words. FedMA puts all the pieces of the model together by matching and combining similar parts from each device. FedMA works better than other ways of doing federated learning and makes it easier for devices to talk to each other. Data bias means that some kinds of data might be more important than others, so we need to be careful when using real-world data to teach computers. Inclusive images are one way people have tried to fix this problem by making sure there are enough different kinds of pictures in the training data.
Definitions
- Federated learning: when devices work together to learn something without sharing their private information.
- Model training: teaching a computer how to do something.
- Cloud: a big network of computers that can store and process lots of information.
- Algorithm: a set of instructions for solving a problem or completing a task.
- Neural network architectures: complicated models that try to copy the way our brains work.
- Convolutional neural networks (CNNs): special kinds of neural networks used for working with pictures.
- LSTMs: another kind of neural network used for working with sequences (like words or music).
- Hidden elements: parts of the model that we can't see directly but help it work better.
- Feature extraction signatures: patterns in the data that help us understand what's important
Federated Learning with Federated Matched Averaging (FedMA)
In recent years, federated learning has emerged as a promising approach to enable edge devices to collaboratively learn a shared model while keeping the training data on device. This decouples the ability to do model training from the need to store the data in the cloud. To this end, Federated matched averaging (FedMA) algorithm has been proposed for federated learning of modern neural network architectures such as convolutional neural networks (CNNs) and LSTM networks. In this blog article, we will discuss how FedMA works, its performance compared to existing state-of-the-art methods, and techniques for addressing data bias in machine learning models trained on real-world datasets.
How Does FedMA Work?
The FedMA algorithm constructs a shared global model in a layer-wise manner by matching and averaging hidden elements with similar feature extraction signatures. Specifically, it matches channels for convolution layers, hidden states for LSTM layers, and neurons for fully connected layers. The idea behind this is that if two elements have similar feature extraction signatures then they are likely to be more relevant than those with different signatures. By matching and averaging these elements across multiple devices participating in federated learning process, FedMA can construct an accurate global model without needing access to all of the training data stored on each device or requiring excessive communication between devices during training process.
Performance Evaluation
The performance of FedMA has been evaluated through experiments on deep CNN and LSTM architectures trained on real-world datasets such as CIFAR-10 dataset. Table 1 shows that FedMA achieves a final accuracy of 87.53% for VGG-9 trained on CIFAR-10 dataset compared to 86.29% achieved by FedAvg and 85.32% achieved by FedProx Ensemble methods:
Table 1: Performance comparison of various algorithms
AlgorithmAccuracy (%)
FedAvg86.29
FedProx Ensemble 85.32
< td >< b >Fed MA < td >< b >87 . 53 < / tr >
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These results indicate that not only does FedMA outperform popular state-of-the art federated learning algorithms but also reduces overall communication burden during training process due its layer wise construction mechanism which allows it to match similar features across multiple devices without having access all of their respective training data sets or requiring excessive communication between them during training process .
Addressing Data Bias Issues
Addressing data bias is an important aspect of machine learning models since real - world data often exhibit multimodality within each class leading to biases in classification models trained on such data . To address this issue , various techniques have been proposed such as inclusive images ( Doshi 2018 ) which uses generative adversarial networks ( GANs ) along with active learning techniques like uncertainty sampling , query synthesis , etc . ,to generate additional samples from underrepresented classes thereby reducing bias present in original dataset . In conclusion , Federated matched averaging algorithm offers a promising solution for federated learning of modern neural network architectures while reducing communication burden and achieving better performance than existing state - of - the - art methods . Furthermore , addressing data bias is essential for improving generalizability of machine learning models trained on real - world datasets .
