Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 1037-1042· 0 citations· 13 references
Abstract
We present a comprehensive empirical study of federated learning (FL) for seven heterogeneous autonomous driving models and propose AdaFed, an adaptive selective aggregation strategy. We introduce a tier-based partial backbone sharing framework that groups models by shared architectural components (ResNet-34/50 backbones, bird’s-eye-view (BEV) encoders, and BEV-Lift modules) and evaluate six FL strategies: FedAvg, FedProx, SCAFFOLD, FedMD, FedDF, and AdaFed. Across all seven models, no FL strategy surpasses the per-model imitation-learning (IL) baseline; the practical question is therefore which strategy minimises degradation. AdaFed applies a tier-specific base strategy, blends the aggregated and local weights with an adaptive coefficient, and reverts any round-level update that worsens validation error. Experiments on CARLA 0.9.16 reveal that (1) partial backbone averaging stays closest to the IL baseline for ResNet-50 models (+0.05–0.13 m average displacement error (ADE)), (2) knowledge-distillation (KD) based approaches degrade performance, and (3) AdaFed yields the best FL result for the two models most exposed to negative transfer—VAD (2.05 m, −0.26 m vs. the best conventional FL strategy) and UniAD (1.52 m). These findings provide practical guidelines for heterogeneous FL deployment in autonomous driving.
This article presents the first dedicated and comprehensive survey of DRL for Open AI-RAN, and reviews the foundations of model-free, model-based, offline, safe, multi-agent, federated, and transfer learning, and provides an O-RAN-aware framework for formulating RAN control problems through states, observations, actions, rewards, constraints, and temporal structure.
Jie Lu, Peihao Yan, Qijun Wang et al.· 0 citations
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
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
Zhiqiang Xie, Yijing Lin, Zhipeng Gao et al.· IEEE Transactions on Cogniti...· 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.
Although autonomous, Large Language Model (LLM)-driven systems show immense potential for orchestrating complex scientific experiments, their efficacy is constrained by two fundamental bottlenecks: context dilution, where strategic reasoning degrades as experimental history accumulates, and inter-campaign amnesia, which forces systems into computationally expensive tabula rasa explorations upon encountering novel domains. To overcome these limitations, we introduce the Multi-Objective State-Action Network (MOSAN), an autonomous cognitive information fusion framework designed for robust cross-campaign generalization and sensor shift adaptation. MOSAN achieves effective information fusion by coupling a self-evolving semantic memory with an Organic Strategic Graph Memory (OSGM), strictly orchestrated through a novel Single Ledger Architecture (SiLA) that isolates cognitive phases and prevents context saturation. A core mechanism of the OSGM is Cross-Domain Strategic Seeding ("ghost node" injection), a transient cold-start fusion strategy. Upon encountering novel datasets, the OSGM temporarily injects topological priors from similar past domains; these ephemeral nodes guide initial architectural deductions and are subsequently purged, allowing the system to disseminate structural knowledge across disciplines while strictly guaranteeing zero data contamination. The fusion framework was validated on the challenging task of bacterial classification via Surface-Enhanced Raman Spectroscopy (SERS) subject to sensor aging shifts, autonomously discovering highly accurate multi-topological architectures. Crucially, when subjected to an out-of-distribution 1D sequential modality (the ECG5000 cardiovascular dataset), the agent performed zero-shot architectural meta-learning, bypassing brute-force search to rapidly achieve 0.9930 accuracy. Finally, the framework was successfully deployed using an open-weight model (Mistral 24B) at zero API inference cost. By continuously fusing multi-epoch empirical evidence, bridging representational topologies, and actively overcoming sensor drift, MOSAN establishes a scalable and rigorous paradigm for autonomous biophysical discovery.
D. Sagnelli, B. Guilcapi, A. Milano et al.· Machine Learning: Science an...· 0 citations
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