Learn From Others and Be Yourself in Federated Human Activity Recognition via Attention-Based Pairwise Collaborations

Federated learning has recently been an emerging learning paradigm for training deep neural networks for activity recognition on resource-limited portable devices such as smartphones. However, prior most works typically focus on training a single global model while ignoring potential intrinsic relationship between clients, which cannot generalize well in heterogeneous federated human activity recognition (HAR) scenario. To address this issue, this article proposes an alternative via attention-based pairwise collaborations, where each client can collaborate with other similar clients to generate a stronger personalized model per client-specific objectives. Different from previous most works, instead of aggregating a single model, we adopt an attention method to calculate an optimal weighted model combination for each client, based on figuring out to what extent a client could benefit from other clients. Extensive experiments on four public HAR benchmark datasets with non-IID data setting show that our method significantly outperforms several popular state-of-the-art federated learning baselines in terms of overall accuracy while saves more than 50% communication overhead. Several ablation analyses are conducted to provide a deeper insight into the proposed algorithm in federated learning scenario. In particular, we analyze how the pairwise collaborations iteratively encourage similar clients to have stronger collaboration than inconsistent clients with heterogeneous activity data. An actual implementation is evaluated in a network of resource-limited Internet-of-Things (IoT) devices.