Federated Learning-based AITP for 6G Networks

• The paper presents a novel protocol integrating federated learning into adaptive transmission to improve 6G network scalability, privacy, and performance.
• It employs local SGD with differential privacy and secure aggregation to dynamically adjust PHY and MAC parameters based on real-time device data.
• Experimental results demonstrate reduced latency and energy consumption while boosting throughput and robustness across large-scale, heterogeneous networks.

The Federated Learning-based Decentralized Adaptive Intelligent Transmission Protocol (AITP) is a protocol framework designed to address privacy, scalability, and adaptability challenges in 6G wireless networks by leveraging federated learning (FL) in a decentralized, adaptive, and privacy-preserving architecture. AITP enables user-centric, on-device learning of transmission strategies, real-time adjustment of physical layer (PHY) and medium access control (MAC) parameters, and robust communication among a massive number of edge devices—while maintaining stringent privacy constraints and optimizing key network performance indicators (Ahmed, 20 Dec 2025, Xing et al., 2020).

1. Network Architecture and System Model

AITP operates over a heterogeneous 6G network comprising edge devices—such as smartphones, IoT sensors, and vehicles—coordinated by edge servers or aggregators. The architecture supports two primary modes:

• A multi-aggregator or clustered model, in which several edge servers coordinate FL rounds.
• A fully decentralized peer-to-peer (P2P) architecture where devices directly exchange model updates.

Communication takes place over high-capacity 6G channels (e.g., mmWave, THz), with P2P links among devices for decentralized model exchange, and an optional distributed ledger (blockchain/DLT) layer for logging and verifying model update transactions (Ahmed, 20 Dec 2025). The system supports local learning on device data $D_i$, preserves privacy by never exporting raw data off-device, and can dynamically adjust between central or distributed FL aggregation as dictated by performance and robustness requirements.

2. Federated Learning Workflow and Privacy Mechanisms

Each AITP round includes the following steps:

1. Aggregators (or a P2P mechanism) broadcast the current global model $W^{(t)}$ to selected devices.
2. Devices perform local training on $D_i$, executing local SGD or other update rules to produce $\Delta w_i^{(t)}$.
3. Each device applies a privacy mechanism before transmission:
   • Differential privacy (DP): additive noise $\mathcal{N}(0,\sigma^2)$ to the update.
   • Homomorphic encryption (HE): encrypts $\Delta w_i$ for aggregation without exposure.
   • Secure aggregation (SA): utilizes multi-party computation so only the sum of updates is revealed (Ahmed, 20 Dec 2025).
4. Aggregated updates are weighted (e.g., FedAvg) or merged by blockchain consensus to yield $W^{(t+1)}$.
5. The new global model guides each device's adaptive protocol stack—transmit power $P_i$, modulation and coding scheme $\mathrm{MCS}_i$, and beamforming vectors $V_i$.

Data privacy is robustly preserved by never transmitting raw device data. Attainable privacy budgets (e.g., $\varepsilon \approx 1.75$ for DP) outperform centralized approaches (Ahmed, 20 Dec 2025). Secure aggregation adds minimal computational overhead (~7%).

3. Mathematical Optimization and Algorithmic Foundations

AITP formulates a multi-objective optimization that balances latency $L$, throughput $T$, and energy efficiency $E$ under privacy and operational constraints. The central objective is: $$\min_{\{P_i,\mathrm{MCS}_i,V_i,w_i\}} \; \mathcal{J} = \alpha L_{\text{total}}(W) + \beta (1 - T_{\text{total}}(W)) + \gamma E_{\text{total}}(W)$$ where $W$ aggregates all local model weights $w_i$, and $\alpha,\beta,\gamma$ are KPI weights.

Key constraints include:

• Privacy: $\mathrm{Priv}(\Delta w_i) \leq \varepsilon_{\max}$
• Bandwidth: $$B_i^{\text{update}} + B_i^{\text{data}} \leq B_{i,\max}$$
• Energy: $$P_i T_{\text{tx},i} + E^{\text{comp}}_i \leq E_{i,\max}$$
• Model Accuracy: $$\mathrm{Accuracy}(W^{(R_{\max})}) \geq \mathrm{Acc}_{\mathrm{target}}$$ (Ahmed, 20 Dec 2025).

Local updates follow: $$w_i^{(t+1)} = w_i^{(t)} - \eta \nabla F_i(w_i^{(t)}; D_i)$$ where $F_i$ is the local loss and $\eta$ is the step size.

Periodic consensus is enabled via peer averaging or server aggregation. In D2D-centric models, this takes the form of decentralized SGD, with consensus steps given as: $$w_i^{t+1} \gets \sum_{j\in\mathcal{N}_i\cup\{i\}} a_{ij} w_j^{t+1}$$ where $A=[a_{ij}]$ is a doubly-stochastic weight matrix matching the network's connectivity graph (Xing et al., 2020).

4. Adaptive Transmission and Scheduling

The global FL model provides mappings to device transmission parameters according to locally measured conditions: $$\begin{aligned} \mathrm{MCS}_i^{(t)} &= f_{\mathrm{mcs}}(W^{(t)}, \mathrm{CQI}_i) \ P_i^{(t)} &= f_{\mathrm{power}}(W^{(t)}, g_i, I_i) \ V_i^{(t)} &= f_{\mathrm{beam}}(W^{(t)}, \mathrm{AoA}_i, \mathrm{AoD}_i) \end{aligned}$$ where CQI is the channel quality indicator, $g_i$ channel gain, $I_i$ interference, and AoA/AoD the angle of arrival/departure (Ahmed, 20 Dec 2025).

Transmission adaptation is achieved via control-theoretic feedback, e.g.,

$$P_i^{(t+1)} = P_i^{(t)} + \kappa (T_{\text{target}} - T_i(P_i^{(t)}))$$

with $\kappa$ a step size and $T_i$ the current throughput estimate.

Scheduling in fully decentralized wireless D2D networks is performed via graph coloring to avoid interference; time-frequency resources are partitioned according to the chromatic number of an auxiliary graph $\mathcal{G}^d$ constructed from device connectivity (Xing et al., 2020). Devices adaptively select between digital and analog physical-layer consensus aggregation, based on measured CSI, sparsity ratio, and estimated SNR. Quantized digital transmission is used at high SNR, while compressed analog (over-the-air) aggregation is employed at lower SNRs or high model sparsity, with compressed sensing recovery techniques (e.g., LASSO/OMP) implemented at receivers (Xing et al., 2020).

A high-level system block diagram is as follows: [Local SGD] → [Error Compensation & Sparsifier] → [Scheduler & Mode Selector] → [Digital Modem (Quantizer+FEC) or Analog Modem (Compressor+Pre-equalizer)] → [Wireless Channel] → [Demodulation/Decoding (Digital) or CS Recovery (Analog)] → [Consensus Averager] → [Model Updater].

5. Performance Evaluation and Comparative Analysis

AITP has been empirically evaluated against baseline protocols:

• Centralized AI Protocol (CAIP)
• Non-Adaptive Protocol (NAP)

Selected results for $N=500$ devices (Ahmed, 20 Dec 2025):

Metric	AITP	CAIP	NAP	Improvement (AITP)
Latency (ms)	9.98	10.28	14.04	–2.87% (vs CAIP) –28.9% (vs NAP)
Throughput (Gbps)	185.9	165.8	90.5	+12.2% (vs CAIP) +105% (vs NAP)
Energy Efficiency	254 bits/J	200 bits/J	156 bits/J	+27% (vs CAIP) +63% (vs NAP)
Privacy Loss (ε)	1.75	1.995	1.2	Lower than CAIP
Robustness Score	0.86	0.65	0.67	1.33×CAIP, 1.29×NAP

Metrics are statistically significant at $p<0.05$ by paired t-tests.

Scalability is demonstrated with graceful performance degradation (scalability factor 1.28–3.06×) as device number increases; privacy protection is quantifiable with a computation overhead of only 7% for secure aggregation (Ahmed, 20 Dec 2025). Robustness is enhanced via P2P sharing and multi-aggregator federation.

6. Scalability, Practical Considerations, and Limitations

AITP’s decentralized design and P2P scheduling naturally extend to very large-scale device populations. However, deployments must address:

• Heterogeneous computational constraints among IoT endpoints, mitigated by model compression and selection of lightweight neural architectures.
• Synchronization and discovery issues, particularly in P2P and asynchronous FL variants.
• Additional communication and consensus latency due to blockchain/DLT layers for auditability and verification (Ahmed, 20 Dec 2025).

Protocol parameter tuning is supported by empirical recommendations (Xing et al., 2020):

Parameter	Typical Value	Note
Learning rate $\eta_0$	0.01	decays as $\eta_t=\eta_0/(1+\kappa t)$
Consensus interval $\tau$	10	tunable [5,20]
Block length $N$	30,000	channel uses per block
Number of slots $M$	≤Δ($\mathcal{G}^d$)+1	typically 4–8
Quantization bits, $b$	8 per nonzero entry	digital aggregation
Sparsity level, $l, k$	0.1·$d$ (digital), 0.4·$d$ (analog)	bits and CS trade-off
SNR thresholds	≥15 dB (digital), ≥10 dB (analog)	robust consensus choice

7. Future Research Directions

Suggested enhancements for AITP include:

• Adaptive DP noise schedules for improved privacy-utility tradeoff.
• Secure multi-party computation and threshold HE for aggregator compromise resilience.
• Split/hybrid FL approaches offloading heavy computation to powerful edge nodes.
• End-to-end cross-layer optimization using reinforcement learning for joint PHY/MAC/routing adaptation.
• Real-world 6G testbed deployments, potentially incorporating quantum-safe cryptography for advanced security (Ahmed, 20 Dec 2025).

Consideration of asynchronous FL techniques may further reduce latency spikes in fully decentralized settings. Integration of proof-of-concept designs and exploration of additional privacy-enhancing technologies remains an active topic for near-term investigation.

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Federated Learning-based Decentralized Adaptive Intelligent Transmission Protocol (AITP) supports scalable, privacy-preserving, and self-optimizing wireless communication in 6G and massive IoT environments. Its architecture, algorithmic constructs, and performance benchmarks establish it as a foundational approach for next-generation, user-centric wireless systems (Ahmed, 20 Dec 2025, Xing et al., 2020).