AdaAFSL: Adaptive Asynchronous Federated Split Learning

A framework for network-aware distributed machine learning that combines Split Learning (SL) with Federated Learning (FL) and adapts communication overhead based on real-time network conditions. The system monitors link utilization via SNMP and dynamically reduces the volume of intermediate activations and gradients exchanged between clients and the server using stochastic masking and quantization.

What It Does

AdaAFSL addresses the communication bottleneck in distributed learning over bandwidth-constrained networks (e.g., edge/IoT deployments). It:

1. Splits a neural network between clients (early layers) and a server (later layers), so clients only transmit intermediate representations rather than raw data or full model updates.
2. Monitors network utilization in real time using SNMP polling of switch interfaces to detect congestion.
3. Adaptively compresses the exchanged activations and gradients using Bernoulli masking and FP16 quantization, with the compression ratio driven by current link utilization.
4. Aggregates models asynchronously on the server using a weighted averaging scheme (exponentially decaying client contribution), enabling clients with different computational speeds to participate without synchronization barriers.

How It Works

Architecture

┌──────────────┐ Socket (TCP) ┌──────────────────────┐
│ Client(s) │ ───── activations/grads ────▶ │ Server │
│ (c_LeNet / │ ◀──── gradients/model ─────── │ (s_LeNet / s_ResNet │
│ c_ResNet) │ │ + aggregation) │
└──────────────┘ └──────────────────────┘
│ │
│ SNMP Monitoring │
└──────────────── Switch ────────────────────────┘

• Client side: Runs the first few layers of the model, encodes intermediate activations (with adaptive masking), and sends them to the server. Receives compressed gradients back for local backpropagation.
• Server side: Runs the remaining layers, computes the loss, performs backpropagation on its portion, and returns gradients to the client. Periodically aggregates model weights from all connected clients.
• Network monitor: Continuously polls SNMP counters on the network switch to compute per-link utilization. This drives the reduction factor p (probability of zeroing out activations).

Supported Algorithms

Algorithm	Description
AdaAFSL	Adaptive Asynchronous Federated Split Learning (network-aware compression + Asynchronous updates)
AFSL	Asynchronous Federated Split Learning (Asynchronous updates)
FSL	Federated Split Learning (fixed split, no adaptive compression)
SL	Standard Split Learning
PSL	Parallel Split Learning

Supported Datasets

• MNIST — Handwritten digit classification
• CIFAR-100 — 100-class image classification (uses ResNet)
• 5G — 5G network traffic classification
• IOT — IoT device traffic regression/classification
• VOD — Video-on-Demand QoE prediction

Network Stress Testing

The Stresser/ module uses iperf3 to generate controlled background traffic on the network, simulating congestion scenarios for evaluation purposes.

Project Structure

├── server.py # Entry point to launch the server
├── server_threshold.py # Server launcher for threshold-finding experiments
├── eval_server.py # Entry point for evaluation
├── requirements.txt # Python dependencies
├── AFSL/ # Server-side algorithm implementations
│ ├── server_model_AFSL_*.py # AFSL server for each dataset
│ ├── server_model_FSL_*.py # FSL server for each dataset
│ ├── server_model_SL_*.py # SL server for each dataset
│ ├── models.py # Neural network architectures (LeNet, ResNet, MLP, CNN)
│ ├── utils.py # Communication, encoding/decoding, SNMP utilities
│ ├── args.py # Configuration and hyperparameters
│ └── test.py # Test/evaluation functions
├── Client/ # Client-side implementations
│ ├── client_model_*.py # Client training scripts per dataset
│ ├── models.py # Client-side model architectures
│ ├── utils.py # Client communication utilities
│ ├── preprocess_data.py # Data loading and preprocessing
│ └── args.py # Client configuration
├── Stresser/ # Network stress-testing tools (iperf3-based)
├── RantopK/ # RandTopK sparsification baseline
├── RTT/ # Round-trip time measurement utilities
└── batch_scripts/ # PowerShell scripts for experiment orchestration

Installation

Prerequisites

• Python 3.8+
• A network environment with SNMP-enabled switches (for network monitoring)
• iperf3 (for stress testing experiments)
• scapy — used as an external process for packet capture but not provided by this repository; install separately (pip install scapy)
• CUDA-capable GPU (optional, falls back to CPU)

Setup

# Clone the repository
git clone <repository-url>cd distributedml
# Create a virtual environment (recommended)
python3 -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt

Configuration

Edit the configuration files to match your network topology:

• AFSL/args.py — Server-side settings (IP addresses, switch ports, SNMP credentials, hyperparameters)
• Client/args.py — Client-side settings

Key parameters in AFSL/args.py:

• switch_port: Mapping of device IPs to switch interface indices
• snmp_host / snmp_community: SNMP agent address and community string
• reduction_threshold: Per-dataset maximum compression ratio
• version: 0 = no compression, 1 = uplink only, 2 = uplink + downlink

Usage

Running the Server

python3 server.py

The server script launches the selected algorithm (configured inside server.py):

• algorithm: one of AFSL, FSL, PSL, SL
• dataset: one of MNIST, CIFAR100, 5G, IOT, VOD

Running a Client

On each client device:

python3 Client/client_model_CIFAR100.py <server_ip><num_local_clients><local_id><delay><seed><batch_size><port><input_size><start_delay>

Finding Compression Thresholds

python3 server_threshold.py <probability>

This runs the training with a constant reduction probability to determine the accuracy-compression trade-off.

Evaluation

python3 eval_server.py

Results

Experiment results are saved under results/Experiment_<id>/ with the following structure:

• accuracy/ — Per-epoch accuracy and loss
• data_transfer/ — Bytes sent/received per client, reduction factors
• Network_Monitoring/ — Per-interface utilization logs
• resources/ — Model sizes, aggregation contributions
• epoch_time/ — Training timing breakdowns

Citation

If you use this code in your research, please cite:

@article{author2025afsl,
title = {AFSL: Adaptive Federated Split Learning with Network-Aware Communication Reduction},
author = {TODO: Author Names},
journal = {TODO: Journal/Conference Name},
year = {TODO: Year},
volume = {TODO},
pages = {TODO},
doi = {TODO}
}

License

TODO: Specify license.