Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning

• URL: http://arxiv.org/abs/2410.09099v1
• Date: Wed, 9 Oct 2024 10:43:29 GMT
• Title: Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning
• Authors: Anastasiya Danilenka, Alireza Furutanpey, Victor Casamayor Pujol, Boris Sedlak, Anna Lackinger, Maria Ganzha, Marcin Paprzycki, Schahram Dustdar,
• Abstract summary: We introduce a conceptual agent for heterogeneous pervasive systems that permits setting global systems constraints as high-level SLOs. We conduct experiments on a physical testbed of devices with different resource types and vendor specifications. The AIF agent can balance competing SLOs in resource heterogeneous environments to ensure up to 98% fulfillment rate.
• Score: 4.274943486546923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Handling heterogeneity and unpredictability are two core problems in pervasive computing. The challenge is to seamlessly integrate devices with varying computational resources in a dynamic environment to form a cohesive system that can fulfill the needs of all participants. Existing work on systems that adapt to changing requirements typically focuses on optimizing individual variables or low-level Service Level Objectives (SLOs), such as constraining the usage of specific resources. While low-level control mechanisms permit fine-grained control over a system, they introduce considerable complexity, particularly in dynamic environments. To this end, we propose drawing from Active Inference (AIF), a neuroscientific framework for designing adaptive agents. Specifically, we introduce a conceptual agent for heterogeneous pervasive systems that permits setting global systems constraints as high-level SLOs. Instead of manually setting low-level SLOs, the system finds an equilibrium that can adapt to environmental changes. We demonstrate the viability of AIF agents with an extensive experiment design, using heterogeneous and lifelong federated learning as an application scenario. We conduct our experiments on a physical testbed of devices with different resource types and vendor specifications. The results provide convincing evidence that an AIF agent can adapt a system to environmental changes. In particular, the AIF agent can balance competing SLOs in resource heterogeneous environments to ensure up to 98% fulfillment rate.

Related papers

• Client-Centric Federated Adaptive Optimization [78.30827455292827]
  Federated Learning (FL) is a distributed learning paradigm where clients collaboratively train a model while keeping their own data private. We propose Federated-Centric Adaptive Optimization, which is a class of novel federated optimization approaches.
  arXiv  Detail & Related papers  (2025-01-17T04:00:50Z)
• A Multi-AI Agent System for Autonomous Optimization of Agentic AI Solutions via Iterative Refinement and LLM-Driven Feedback Loops [3.729242965449096]
  This paper introduces a framework for autonomously optimizing Agentic AI solutions across industries. The framework achieves optimal performance without human input by autonomously generating and testing hypotheses. Case studies show significant improvements in output quality, relevance, and actionability.
  arXiv  Detail & Related papers  (2024-12-22T20:08:04Z)
• SLA Management in Reconfigurable Multi-Agent RAG: A Systems Approach to Question Answering [0.0]
  Real-world applications impose diverse Service Level Agreements (SLAs) and Quality of Service (QoS) requirements. We present a systems-oriented approach to multi-agent RAG tailored for real-world Question Answering (QA) applications.
  arXiv  Detail & Related papers  (2024-12-07T01:32:13Z)
• Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Environmental Uncertainty [40.55653383218379]
  This work focuses on learning in distributionally robust Markov games (RMGs) We propose a sample-efficient model-based algorithm (DRNVI) with finite-sample complexity guarantees for learning robust variants of various notions of game-theoretic equilibria.
  arXiv  Detail & Related papers  (2024-04-29T17:51:47Z)
• HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments [93.94020724735199]
  HAZARD consists of three unexpected disaster scenarios, including fire, flood, and wind. This benchmark enables us to evaluate autonomous agents' decision-making capabilities across various pipelines.
  arXiv  Detail & Related papers  (2024-01-23T18:59:43Z)
• Effective Intrusion Detection in Heterogeneous Internet-of-Things Networks via Ensemble Knowledge Distillation-based Federated Learning [52.6706505729803]
  We introduce Federated Learning (FL) to collaboratively train a decentralized shared model of Intrusion Detection Systems (IDS) FLEKD enables a more flexible aggregation method than conventional model fusion techniques. Experiment results show that the proposed approach outperforms local training and traditional FL in terms of both speed and performance.
  arXiv  Detail & Related papers  (2024-01-22T14:16:37Z)
• AdaptiveFL: Adaptive Heterogeneous Federated Learning for Resource-Constrained AIoT Systems [25.0282475069725]
  Federated Learning (FL) is promising to enable collaborative learning among Artificial Intelligence of Things (AIoT) devices. This paper introduces an effective FL approach named AdaptiveFL based on a novel fine-grained width-wise model pruning strategy. We show that AdaptiveFL can achieve up to 16.83% inference improvements for both IID and non-IID scenarios.
  arXiv  Detail & Related papers  (2023-11-22T05:17:42Z)
• Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning [55.08287089554127]
  Open Radio Access Network systems, with their base stations (vBSs), offer operators the benefits of increased flexibility, reduced costs, vendor diversity, and interoperability. We propose an online learning algorithm that balances the effective throughput and vBS energy consumption, even under unforeseeable and "challenging'' environments. We prove the proposed solutions achieve sub-linear regret, providing zero average optimality gap even in challenging environments.
  arXiv  Detail & Related papers  (2023-09-04T17:30:21Z)
• FS-Real: Towards Real-World Cross-Device Federated Learning [60.91678132132229]
  Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data. There is still a considerable gap between the flourishing FL research and real-world scenarios, mainly caused by the characteristics of heterogeneous devices and its scales. We propose an efficient and scalable prototyping system for real-world cross-device FL, FS-Real.
  arXiv  Detail & Related papers  (2023-03-23T15:37:17Z)
• Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning [71.53769213321202]
  We formalize the notions of coordination level and heterogeneity level of an environment. We present HECOGrid, a suite of multi-agent environments that facilitates empirical evaluation of different MARL approaches. We propose a Training Decentralized Execution learning approach that enables agents to work efficiently in high-coordination and high-heterogeneity environments.
  arXiv  Detail & Related papers  (2022-10-04T18:17:01Z)
• Multi-Scale Asset Distribution Model for Dynamic Environments [1.370633147306388]
  We study the impact that the topology of the multi-scale control process has upon the system's ability to self-adapt asset distribution. Results show how different topological characteristics and different competition levels between system branches impact overall system profitability.
  arXiv  Detail & Related papers  (2022-07-25T11:14:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.