For the 5th InternationalWorkshop on Active Inference (IWAI), we gathered in Oxford, UK on September 9–11, 2024. Around 130 active inference researchers from academia and industry attended the workshop in the beautiful Corpus Christi College, with a threeday program packed with two tutorials, six keynotes, a poster session, and seven sessions of paper presentations.

This volume presents the 17 full papers that were accepted and presented at the workshop. Out of 54 submissions, 17 full papers were selected through a double-blind review process. These papers cover a wide range of domains that find applications of active inference, ranging from robotics, decision-making and control, psychology, and representation learning, to theoretical advancements of learning and inference as well as active inference implementations. We also selected 33 contributions to be presented as posters.

The IWAI 2024 organizers would like to thank the Program Committee for their valuable review work, all authors for their contributions, Charel van Hoof and Sanjeev Namjoshi for their excellent tutorials, Emma Holmes, Chris Buckley, Rafal Bogacz, Pablo Lanillos, Ingmar Posner and Karl Friston for their inspiring keynotes, and of course all the attendees. We would also like to thank our sponsor VERSES AI, which made this event possible. [...]

The demand for artificial intelligence has grown significantly over the past decade, and this growth has been fueled by advances in machine learning techniques and the ability to leverage hardware acceleration. However, to increase the quality of predictions and render machine learning solutions feasible for more complex applications, a substantial amount of training data is required. Although small machine learning models can be trained with modest amounts of data, the input for training larger models such as neural networks grows exponentially with the number of parameters. Since the demand for processing training data has outpaced the increase in computation power of computing machinery, there is a need for distributing the machine learning workload across multiple machines, and turning the centralized into a distributed system. These distributed systems present new challenges: first and foremost, the efficient parallelization of the training process and the creation of a coherent model. This article provides an extensive overview of the current state-of-the-art in the field by outlining the challenges and opportunities of distributed machine learning over conventional (centralized) machine learning, discussing the techniques used for distributed machine learning, and providing an overview of the systems that are available.