Summary of Adaptive Active Inference Agents For Heterogeneous and Lifelong Federated Learning, by Anastasiya Danilenka et al.
Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning
by Anastasiya Danilenka, Alireza Furutanpey, Victor Casamayor Pujol, Boris Sedlak, Anna Lackinger, Maria Ganzha, Marcin Paprzycki, Schahram Dustdar
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed for designing adaptive agents in pervasive computing systems, which seamlessly integrates devices with varying computational resources. The existing work on adaptive systems typically focuses on optimizing individual variables or low-level Service Level Objectives (SLOs), introducing complexity in dynamic environments. This paper introduces a conceptual agent based on Active Inference (AIF) framework, setting global system constraints as high-level SLOs, allowing the system to adapt to environmental changes. The proposed AIF agents demonstrate viability through extensive experiments using heterogeneous and lifelong federated learning as an application scenario, achieving up to 98% fulfillment rate in resource-heterogeneous environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Pervasive computing devices with different resources can work together seamlessly by adapting to changing conditions. This is a big challenge because existing solutions focus on individual parts of the system rather than the whole. The authors propose using Active Inference, a way to design smart agents that adapt to their environment. They introduce a new kind of agent that sets overall goals for the system and finds an equilibrium that can change with the environment. This is demonstrated through experiments using devices with different resources and learning techniques. |
Keywords
» Artificial intelligence » Federated learning » Inference
