2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 9867-9882· 0 citations· 51 references
Computer Science
Abstract
In agentic AI-enabled edge computing, decentralized federated learning (DFL) leverages peer-to-peer model aggregation to improve the performance of on-device large language models (LLMs) without introducing a single point of failure, thereby enhancing local agents’ capabilities for decision-making. To protect the right to be forgotten for each agent, as required by data regulations such as the General Data Protection Regulation (GDPR), federated unlearning aims to remove the influence of a target agent’s data from the trained LLM while preserving model utility. However, existing federated unlearning methods predominantly assume centralized architectures and face two critical challenges when extended to decentralized federated learning systems: 1) requiring all remaining agents to participate in the unlearning process incurs prohibitive overhead; and 2) updating all Low-Rank Adaptation (LoRA) modules indiscriminately leads to excessive resource consumption while potentially degrading model performance. To tackle these challenges, we propose a dual-level selective unlearning framework (DSU) for decentralized federated learning. At the agent level, DSU selects retained agents by matching historical LoRA update sketches, label sketches, and exposure to the withdrawn agent. Meanwhile, we derive a sensitivity score from the DFL training trajectory that measures each LoRA module’s accumulated influence from the target agent via the energy of its effective weight changes, and then update only the most sensitive modules while freezing the rest. Theoretical analysis relates selective unlearning to full-participation unlearning, and experiments show that DSU reduces the audited target-agent influence while preserving retained utility and reducing both participating agents and updated LoRA modules in the evaluated settings. The public reproducibility package is available at https://github.com/DGL-codes/LLM-DFL
FedRAM is proposed, a three-step framework that progressively updates two scalar hyperparameters: the task importance weight and the client aggregation coefficient, where the proxy model serves as an intermediate between the local reference model and the global agent model.
Fan Wu, Xinyu Yan, Jiabei Liu et al.· Neural Information Processin...· 0 citations
Decentralized intelligence systems with heterogeneous devices and limited coordination increasingly rely on decentralized federated learning (DFL). However, DFL suffers from convergence inefficiency under data heterogeneity due to the use of a uniform learning rate (LR) that ignores layer-specific optimization needs. Foundational layers are responsible for maintaining network consensus, while specialized layers adapt to local data characteristics, leading to conflicting gradients and degraded performance under non-IID conditions. To address this fundamental tension, this work introduces FedA2L, a method that dynamically adjusts layer-wise LRs based on model divergence signals. By leveraging local update intensity and network consensus constraints, FedA2L seamlessly integrates into existing DFL protocols without additional communication or coordination. Extensive evaluations across DFL algorithms, various model architectures, and datasets demonstrate that FedA2L achieves up to 4.94 times faster convergence than vanilla DFL and reduces communication rounds by up to 59% compared to scheduler-based baselines. Furthermore, FedA2L exhibits resilience to severe data heterogeneity, larger network sizes, and sparse topologies, reducing communication overhead and establishing it as a versatile optimization tool for resource-constrained or large-scale distributed learning in edge and IoT deployments. The code is released at https://github.com/nclabteam/FedA2L.
V. T. Vo, K. Nguyen, Taehong Kim· Future generations computer...· 0 citations
FedJigsaw is proposed, a novel framework that reshapes model personalization as a dynamic and decentralized model assembly problem that outperforms state-of-the-art MHFL baselines by up to 13.8% in relative accuracy while significantly shrinking cross-client performance variance, but also slashes decision-making latency and peak memory footprint compared to existing policy-driven methods.
Multi-agent systems (MAS) are enabling increasingly complex, collaborative applications in autonomous driving, smart logistics, robotic coordination, and distributed sensing. Their effectiveness depends on collective intelligence emerging from multiple distributed agents, each operating with partial information and often sensitive local data. To realize such collaboration while preserving data privacy and autonomy, federated learning (FL) has emerged as a de facto decentralized framework that allows agents to learn shared models without centralizing raw data. Despite the rapid progress in both FL and MAS, significant challenges remain in integrating these paradigms—such as coordinating heterogeneous agents, handling non-IID data, ensuring communication efficiency, and maintaining system robustness and fairness in open environments. This workshop seeks to bring together researchers and practitioners from academia and industry to explore the convergence of federated learning and multi-agent systems. We aim to foster discussions on foundational advances, real-world deployments, and emerging interdisciplinary opportunities, with a focus on scalability, trustworthiness, adaptive coordination, and the broader societal impact of federated multi-agent intelligence.
Haozhao Wang, Zhuangdi Zhu, Zheng Xu et al.· Proceedings of the 32nd ACM...· 0 citations
Decentralized federated learning (DFL) is a promising paradigm for autonomous nodes to collaboratively train AI models without relying on a central server. However, existing DFL solutions do not guarantee global model consistency, a critical requirement for collaborative mission-critical scenarios where model divergence undermines decision uniformity and safety. This lack of consistency also amplifies vulnerability to Byzantine adversaries, who exploit the decentralized network topology and weak synchrony to perform equivocation and model poisoning attacks against individual victims. This paper introduces DFL-C, a novel Byzantine-resilient DFL architecture that enables decentralized nodes to perform collaborative training with global model consistency. At its core, DFL-C integrates an asynchronous common subset (ACS) consensus protocol into the DFL workflow to ensure all nodes aggregate a uniform set of model updates to establish global model consistency, despite individual Byzantine equivocation. DFL-C further implements a dual-domain trust scoring mechanism to provide resilience against data-domain Byzantine manipulations including model poisoning attacks. This mechanism complements the consensus protocol, significantly reducing the latter's runtime. Our experimental results demonstrate that DFL-C maintains model accuracy while achieving global model consistency under Byzantine behaviors with moderate consensus overhead. Notably, when compared with the state-of-the-art DFL solution BALANCE (Fang et al.) that does not provide model consistency, DFL-C achieves better model accuracy against untargeted model poisoning attacks and comparable resilience against backdoor attacks, with the advantage widened under non-IID scenarios.
Yue Li, Sudip Bhujel, Cameron Lira et al.· 0 citations
Federated learning relies on aggregation schemes that assume all participants train models with identical architectures and a common parameter initialization. While this enables parameter-averaging strategies such as Federated Averaging, it also imposes a strong inductive bias by constraining local models to evolve from nearly identical starting points, potentially reducing model diversity and limiting exploration of the hypothesis space. Recent studies have explored shared-initialization-free and heterogeneous federated learning as largely independent research directions. In this context, we propose a shared-initialization-free, architecture-agnostic aggregation strategy based on meta-learning, where local models generate predictions over a reference dataset that are integrated to train a global meta-model. Among the different forms of heterogeneity in federated learning, this work focuses on statistical and model heterogeneity. The proposed framework is evaluated in both one-shot and multi-round federated settings against parameter-averaging and knowledge-distillation approaches while preserving the independent evolution of local models. Experiments across diverse federated settings, including homogeneous and heterogeneous models, varying numbers of participants, different levels of data imbalance on representative benchmark dataset demonstrate that the proposed strategy in a one-shot scenario achieves competitive performance under limited-data conditions, while iterative refinement of the prediction consensus in the multi-round setting yields consistent improvements over traditional aggregation strategies. These findings demonstrate that prediction-space meta-learning constitutes a practical alternative for federated aggregation without requiring shared parameter initialization. Future work will investigate more advanced consensus mechanisms, adaptive historical consensus strategies, and the evaluation of the proposed framework under larger-scale federated learning environments.
