FedTM: Memory and Communication Efficient Federated Learning with Tsetlin Machine

Federated Learning has been an exciting development in machine learning, promising collaborative learning without compromising privacy. However, the resource-intensive nature of Deep Neural Networks (DNN) has made it difficult to implement FL on edge devices. In a bold step towards addressing this challenge, we present FedTM, the first FL framework to utilize Tsetlin Machine, a low-complexity machine learning alternative. We proposed a two-step aggregation scheme for combining local parameters at the server which addressed challenges such as data heterogeneity, varying participating client ratio and bit-based aggregation. Compared to conventional Federated Averaging (FedAvg) with Convolutional Neural Networks (CNN), on average, FedTM provides a substantial reduction in communication costs by $30.5 \times$ and $36.6 \times$ reduction in storage memory footprint. Our results demonstrate that FedTM outperforms BiFL-BiML (SOTA) in every FL setting while providing $1.37-7.6 \times$ reduction in communication costs and $2.93-7.2 \times$ reduction in run-time memory on our evaluated datasets, making it a promising solution for edge devices.