DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning

Hardware Requirements

CPU with 10+ cores, Nvidia GPU with at least 10 GB VRAM and minimum CUDA 12.2 support, at least 128 GB RAM, and 0.5 TB disk space.

Installation

The following assumes a virtual environment with Python 3.10 installed and bash as the default terminal.

# install dependencies
python -m pip install -r requirements.txt

# create/update python path
echo "PYTHONPATH=$PYTHONPATH:." >> ~/.bashrc
source ~/.bashrc

Datasets

• MNIST / CIFAR-10: Downloaded automatically via torchvision on first run. No manual steps needed.
• CIFAR-100 / CelebA: Downloaded from HuggingFace and partitioned into federated splits. Pre-computed index files (dataset/*-splits.npz) are included in the repo. To build the dataset files, run:

  python src/dataset/build_dataset.py --dataset_name cifar100
  python src/dataset/build_dataset.py --dataset_name celeba

• SST-2: Pre-split data is already included in the repo under dataset/sst2-processed/. No download needed.

Running Experiments

Experiments are configured via src/constants.py. Default parameters are in init_parameters. To run a specific experiment, pass its index from var_params as an argument:

PYTHONPATH=$PYTHONPATH:. python src/disagg_test.py <index>
PYTHONPATH=$PYTHONPATH:. python src/opa_test.py <index>
PYTHONPATH=$PYTHONPATH:. python src/light_secagg_test.py <index>
PYTHONPATH=$PYTHONPATH:. python src/secagg_plus_test.py <index>

For plain-text FL experiments, set USE_SECURITY=False in src/constants.py and use the src/disagg_test.py script.

Index	Description	Recommended num_proc	Scripts to run
0	Quick test (N=100, M=1k)	16	disagg_test.py
1	Set M=N=10k and vary ρ (Figure 6)	16	{disagg|opa}_test.py
2	Sweep M, N with all protocols (Figure 7)	16	{disagg|opa|light_secagg|secagg_plus}_test.py
3	Set M=N=100k and vary ρ (Table 3)	4	{disagg|opa}_test.py
4	Set M=N=10k and vary γ,δ (Figure 8)	16	{disagg|opa}_test.py
5	Train NLP model with SST2 (Table 6)	2	{disagg|opa}_test.py for lora_r=16, disagg_test.py for lora_r=64
6	Train EfficientNet model with CELEBA (Table 6)	2	{disagg|opa}_test.py
7	Train CNN models on MNIST & CIFAR10 (Figure 9)	16	{disagg|opa}_test.py
8	Train TinyNet model on CIFAR100 (Figure 9)	4	{disagg|opa}_test.py
9	Experiment with stragglers on CIFAR10 (Figure 12)	16	{disagg|opa}_test.py

Example — run SST2 experiment with DisAgg:

PYTHONPATH=$PYTHONPATH:. python src/disagg_test.py 5

Experiment customization

Experiments can be customized by modifying the configuration parameters in src/constants.py. Edit init_parameters to change default values, or add new sweep configurations to var_params. Parallelism is controlled by num_proc; set to 0 for sequential execution.

Citation

Mehmood, H., Tatsis, G., Alexopoulos, D., Saravanan, K., Xu, J., Drosou, A., and Ozay, M. DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning. To appear in Proceedings of the Ninth Annual Conference on Machine Learning and Systems, MLSys 2026.