Edge AI Swarm Architecture

• Edge AI-driven decentralized swarm architecture is a distributed paradigm that integrates edge intelligence with swarm principles for in situ learning and self-organization.
• It employs a multi-tier framework combining edge nodes, local aggregators, and cloud coordinators to enable efficient model training and dynamic, peer-to-peer communication.
• The approach enhances scalability, fault tolerance, and privacy through adaptive protocols, practical incentive mechanisms, and real-world applications like UAV swarms and urban mobility.

Edge AI-driven decentralized swarm architecture refers to a multilayered, distributed computational paradigm where autonomous or semi-autonomous edge devices—such as IoT sensors, robotics, UAVs, or mobile nodes—collaboratively execute machine learning, inference, and system optimization tasks via direct or near-range communication without depending on a single central authority. These systems are characterized by peer-to-peer or hierarchical exchange, in situ learning, dynamic self-organization, and strong emphasis on scalability, privacy, and resilience to network volatility. The architecture leverages edge computational intelligence and biological swarm intelligence principles, integrating features such as model-centric aggregation, resource orchestration, elastic membership, and robust incentive mechanisms for collaborative, adaptive, and privacy-preserving learning across heterogeneous and potentially large-scale deployments (Abdelmoniem, 2023, Wang et al., 29 Mar 2024, Wang et al., 2022, Zhou et al., 2020, Agnihotri et al., 14 Dec 2025, Queralta et al., 2020, Samadi et al., 7 Oct 2025, Liu et al., 1 Oct 2025, Yang et al., 2020).

1. Fundamental System Structure and Topologies

A defining characteristic of edge AI-driven decentralized swarms is their compositional multi-tier topology, typically segmented into edge nodes (EN), local aggregators (LA), and cloud coordinators (CC). ENs comprise IoT/mobile devices or robotic agents that perform local data collection, model training, and inference. LAs, generally implemented as micro-data centers or powerful edge servers, maintain secure model vaults, aggregate or index peer models, and serve as intermediaries for clustering and discovery. CCs act as meta-coordinators, maintaining a routable index of LA-level model repositories, managing global policies (e.g., incentives, privacy), and orchestrating cross-domain or federation-wide model exchanges (Abdelmoniem, 2023, Queralta et al., 2020).

Swarm formation is dynamic: devices self-organize into peer groups (PG_k) based on proximity, wireless link characteristics, or semantic task relationships, selecting LAs through lightweight leader-election protocols. These groups persist elastically, allowing nodes to join during periods of local data drift or to depart under power constraints. LAs register with one or more CCs to establish a seamless Edge–to–Cloud continuum, supporting both local autonomy and global model orchestration (Abdelmoniem, 2023).

Table: Core Hierarchical Roles

Layer	Core Function	Example Protocols
Edge Nodes (EN)	Data collection, local training, peer-to-peer update gossip	Asynchronous gossip, DP clipping
Local Aggregator	Peer indexing, aggregation, secure vault, cluster-level discovery	Pub–sub, Shamir aggregation
Cloud Coordinator	Meta-indexing, global orchestration, incentive payout, reconciliation	Global aggregation, RPC

2. Communication, Aggregation, and Learning Protocols

Edge AI swarms utilize advanced message passing and aggregation schemes to reduce bandwidth, mitigate stragglers, and support both synchronous and asynchronous workflows. Typical intra-group communication involves periodic gossip of model fingerprints and deltas, with each EN forwarding a compressed difference to a randomly chosen peer. LAs exploit publish–subscribe paradigms to advertise model availability, and ENs subscribe to relevant topics (Abdelmoniem, 2023).

Model updates are scheduled adaptively: ENs transmit only when $\|\Delta w^{(i)}\| > \epsilon$, and LAs trigger aggregation on time or arrival thresholds. Aggregation is formulated as a weighted averaging operation at both the cluster (LA) and global (CC) level: $$w_{t+1}^{(k)} = \sum_{i\in PG_k}\alpha_i^{(k)} w_t^{(i)}, \quad \sum_{i}\alpha_i^{(k)}=1,\; \alpha_i^{(k)}\ge0$$

$$w_{t+1}=\sum_{k=1}^K\beta_k w_{t+1}^{(k)}, \quad \sum_k\beta_k=1$$

Communication cost functions account for link bandwidth $B_{ij}$, latency $d_{ij}$, and node energy $E_i$: $$C_{\rm comm} = \sum_{i \in EN} \sum_{j \in \mathrm{neigh}(i)} B_{ij} d_{ij} + \lambda \sum_{i \in EN} E_i$$ Sparse schedules—for instance, each EN communicating with only a single peer in gossip—drastically cut overhead.

Swarm learning protocols integrate both AI (gradient-based) and BI (swarm-based) update rules, as exemplified in distributed swarm learning (DSL). Each worker updates its model via a convex blend of PSO-style velocity and SGD: $$w_{i,t+1} = w_{i,t} + \lambda_t[c_0 v_{i,t} + c_1 (w^p_{i,t} - w_{i,t}) + c_2 (w^g_t - w_{i,t})] - (1-\lambda_t)\alpha \nabla F_i(w_{i,t}; D_i)$$ with additional censoring and analog over-the-air aggregation to further reduce the communication burden and synchronize only minimal statistics or selected models (Wang et al., 29 Mar 2024, Wang et al., 2022).

3. Scalability, Elasticity, and Fault Tolerance

Elastic membership is maintained through real-time monitoring of node status, heartbeat protocols, and rapid leader re-election in the event of aggregator failure. LA vault entries are replicated (encrypted) to neighbor LAs to prevent data loss. ENs locally cache global model snapshots for warm restarts after disconnection. Upon network partition, local clusters proceed independently; when connectivity is restored, model reconciliation is performed by minimizing $\|w^A - w^B\|$ between divergent partitions (Abdelmoniem, 2023).

Edge AI swarms provide formal convergence guarantees under convex losses, bounded gradients, and bounded staleness: $$\mathbb{E}[f(w_T)] - f(w^*) \leq O\left(\frac{1}{\sqrt{T}} + \frac{S}{T}\right)$$ where $S$ is the staleness bound. In particle-swarm hybrid frameworks, the expected squared gradient decays at $O(1/T)$ (Wang et al., 29 Mar 2024, Wang et al., 2022). This enables robust scaling to thousands of edge nodes.

Resilience is further enhanced by communication protocols tolerant to dropouts, as in over-the-air analog aggregation (missing nodes appear as added noise), and dynamic adaptation of aggregation structures (tree, mesh, or ring) to shifting edge resource topologies (Yang et al., 2020, Agnihotri et al., 14 Dec 2025).

4. Privacy, Security, and Economic Incentives

Differential privacy is enforced by clipping local updates and applying Gaussian noise before dissemination: $$\Delta w^{(i)} \leftarrow \mathrm{clip}_C(\Delta w^{(i)}) + \mathcal{N}(0, \sigma^2 C^2 I)$$ Secure aggregation at the LA level uses Shamir secret-sharing so unnoised, individual updates are never exposed (Abdelmoniem, 2023). In highly adversarial settings, Byzantine-robust scoring and BEV-style power-control algorithms are deployed to screen or down-weight potentially malicious model contributions (Wang et al., 29 Mar 2024, Wang et al., 2022).

Incentive-compatible architectures compensate ENs via a utility function balancing model utility and cost: $$U_i = \gamma \Delta \mathrm{Acc}_i - \eta C_{\rm comm}^{(i)}$$ where $\Delta \mathrm{Acc}_i$ is the marginal accuracy gain and $C_{\rm comm}^{(i)}$ is communication cost.

Tokenized, blockchain-based schemes, such as PolyLink, combine dynamic pricing (per-token, per-model, market-adjusted) and verifiable inference quality protocols (TIQE), with stake-weighted reward distribution, validator selection, and on-chain consensus for full decentralization and robustness to model and verifier corruption (Liu et al., 1 Oct 2025).

5. Heterogeneity, Adaptivity, and Swarm Intelligence

Edge AI-driven swarms accommodate device and data heterogeneity through various mechanisms, including shared global datasets for scoring and calibration, adaptive worker/model selection, and model-centric resource allocation (Abdelmoniem, 2023, Wang et al., 29 Mar 2024, Wang et al., 2022). Containerized microservice platforms (e.g., ROS2 containers, FPGA overlays) provide modular deployment and migration of AI inference and data pipelines across diverse agents, drones, or robots (Queralta et al., 2020, Agnihotri et al., 14 Dec 2025).

Swarm intelligence is realized through distributed algorithms that blend gradient/subgradient descent with PSO-like social mixing, enabling escape from local minima, rapid adaptation to nonstationary environments, and globally coordinated but locally autonomous behavior (Wang et al., 29 Mar 2024, Samadi et al., 7 Oct 2025). Recent small agent network (SAN) models facilitate system decomposition, allowing agents covering subdomains of the global state and communicating over sparse directed graphs. Theoretical results using Kolmogorov–Arnold and Vitushkin-entropy frameworks demonstrate improved adaptation speed, sample efficiency, and scalability compared to monolithic foundational models (Samadi et al., 7 Oct 2025).

6. Practical Applications and Representative Results

Edge AI–swarm architectures have demonstrated marked improvements in critical applications: urban air mobility (UAM) conflict management, fully autonomous drone swarms, decentralized LLM inference, and large-scale IoT learning. Notable empirical findings across these domains include:

• UAM conflict resolution time reduced by up to 3.8× compared to centralized models; accuracy improved by 10–15%; system remains robust (≥90% success) under 20% random device failure (Agnihotri et al., 14 Dec 2025).
• Decentralized drone swarms achieve near-linear speed-up, <80 ms reconfiguration, and sustained 90% planned mission completion with 30% node failures (Queralta et al., 2020).
• DSL outperforms federated learning in non-i.i.d. environments, achieving faster convergence (e.g., 98.5% accuracy in ~120 rounds on MNIST, with <2% degradation under Byzantine attacks) (Wang et al., 29 Mar 2024, Wang et al., 2022).
• PolyLink enables scalable, crowd-sourced LLM inference at the edge, with practical latency and strong resistance to model and validator adversarial behavior (Liu et al., 1 Oct 2025).
• Small agent network-based (SAN) swarms recover prediction accuracy orders of magnitude faster than foundation models in dynamically evolving environments (Samadi et al., 7 Oct 2025).

These results validate the architectural premise: decentralized, adaptive, Edge AI-driven swarms enable scalable, resilient, and privacy-preserving machine learning across resource- and bandwidth-constrained environments, delivering state-of-the-art performance in safety-critical and operationally diverse domains.