Abstract Federated learning (FL) is an emerging communication-efficient and collaborative learning paradigm of machine learning with privacy guarantees. As these advancements unfold, adapting FL for fairness-aware learning becomes crucial. In this context, we propose a pre-processing fairness and utility (balanced accuracy) enhancing agnostic federated learning framework (Fed-FUEL) that mitigates discrimination embedded in the non-independent identically distributed data. We contribute a novel adaptive data manipulation method that mitigates discrimination embedded in the data at client side during optimization, resulting in an optimized and fair centralized server. This pre-processing approach abstracts the model architecture from the equation, offering a significant advantage in a federated environment. This abstraction not only facilitates a broader application across diverse model architectures without necessitating modifications but also sidesteps the potential complexities and inefficiencies associated with model-specific in-processing methods. Extensive experiments with a range of publicly available datasets demonstrate that our method outperforms the competing baselines in terms of both discrimination mitigation and predictive performance. Our model effectively adapts to both statistical and causal fairness notions, as shown through our experiments.

Time Series Classification (TSC) aims to develop predictive models for discrete target variables using ordered, real-valued attributes. However, existing deep learning approaches face challenges in addressing the inherent dependencies across data dimensions and the dynamic characteristics of time series data, often resulting in insufficient feature extraction and suboptimal classification accuracy. This paper introduces HyperTime, a novel framework designed to overcome these challenges by modeling time series data as hypergraphs, where hyperedges connect arbitrary sets of nodes. HyperTime distinguishes itself by processing time series data into segmented windows, which form nodes in a hypergraph connected by hyperedges. This approach captures complex temporal relationships and dynamics within the data. The framework employs ‘hypernode embedding’, applying an attention mechanism to each time series dimension within a window, and ‘hyperedge embedding’, incorporating an LSTM layer for deeper temporal analysis. A key innovation in HyperTime is the ‘hyper convolution’ operation, which includes both hypernode and hyperedge convolutions. Nodes are convoluted using the embeddings of their associated hyperedges, enriching the learning and understanding of their role within the hypergraph. The hyperedge convolution layer integrates local and global information, aggregating node features to capture complex interactions and relationships. The effectiveness of HyperTime is validated through extensive experiments on 26 datasets from the UEA archive, the Human Activity Recognition (HAR) and PAMAP2 Physical Activity Monitoring (PAM) datasets, and 10 datasets from the UCR archive.

Increasing privacy concerns have led to decentralized and federated machine learning techniques that allow individual clients to consult and train models collaboratively without sharing private information. Some of these applications, such as medical and healthcare, require the final decisions to be interpretable. One common form of data in these applications is multivariate time series, where deep neural networks, especially convolutional neural networks based approaches, have established excellent performance in their classification tasks. However, promising results and performance of deep learning models are a black box, and their decisions cannot always be guaranteed and trusted. While several approaches address the interpretability of deep learning models for multivariate time series data in a centralized environment, less effort has been made in a federated setting. In this work, we introduce FLAMES2Graph, a new horizontal federated learning framework designed to interpret the deep learning decisions of each client. FLAMES2Graph extracts and visualizes those input subsequences that are highly activated by a convolutional neural network. Besides, an evolution graph is created to capture the temporal dependencies between the extracted distinct subsequences. The federated learning clients only share this temporal evolution graph with the centralized server instead of trained model weights to create a global evolution graph. Our extensive experiments on various datasets from well-known multivariate benchmarks indicate that the FLAMES2Graph framework significantly outperforms other state-of-the-art federated methods while keeping privacy and augmenting network decision interpretation.

Deep neural networks, especially the Convolutional Neural Network (CNN) models, have shown promising results in multivariate time series data analysis. However, the predictions of these data-driven black-box models are tough to interpret from a human perspective, making it questionable to trust and rely on the predictions made by these models, specifically for time series data with the append-only feature. This paper proposes a new approach to interpret the CNN outputs by extracting and clustering the activated time series sequences learned from a trained network. These sequences show the representative features for each output label and form interpretable representations from the original time series data. Our approach is the first framework to identify each signal’s role and dependencies, consider all possible combinations of signals in the multivariate time-series input, and visualize the data representative features. Our experiments on the Baydogan’s archive indicate remarkable improvements in the interpretability of the network predictions and relation identification of each input signal to the output label and the channels of the network layers. Furthermore, the conducted experiments confirm that the extracted patterns are representative of the multivariate input and changing them results in a drastic reduction in the prediction accuracy.