# Adaptive Aggregation For Federated Learning

arXiv: https://arxiv.org/abs/2203.12163 · HTML: https://mlsanity.com/paper/2203.12163/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2022.

## Built on by (most cited first, 10 of 10)

- [Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications](https://mlsanity.com/paper/2310.05269/) (2023)
- [FedLesScan: Mitigating Stragglers in Serverless Federated Learning](https://mlsanity.com/paper/2211.05739/) (2022)
- [FLIPS: Federated Learning using Intelligent Participant Selection](https://mlsanity.com/paper/2308.03901/) (2023)
- [LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning](https://mlsanity.com/paper/2405.10968/) (2024)
- [ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations](https://mlsanity.com/paper/2308.10457/) (2023)
- [Training Heterogeneous Client Models using Knowledge Distillation in Serverless Federated Learning](https://mlsanity.com/paper/2402.07295/) (2024)
- [On the Conflict of Robustness and Learning in Collaborative Machine Learning](https://mlsanity.com/paper/2402.13700/) (2024)
- [Apodotiko: Enabling Efficient Serverless Federated Learning in Heterogeneous Environments](https://mlsanity.com/paper/2404.14033/) (2024)
- [WarmFed: Federated Learning with Warm-Start for Globalization and Personalization Via Personalized Diffusion Models](https://mlsanity.com/paper/2503.03110/) (2025)
- [Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning](https://mlsanity.com/paper/2604.22072/) (2026)

## Abstract (arXiv)

Advances in federated learning (FL) algorithms,along with technologies like differential privacy and homomorphic encryption, have led to FL being increasingly adopted and used in many application domains. This increasing adoption has led to rapid growth in the number, size (number of participants/parties) and diversity (intermittent vs. active parties) of FL jobs. Many existing FL systems, based on centralized (often single) model aggregators are unable to scale to handle large FL jobs and adapt to parties' behavior. In this paper, we present a new scalable and adaptive architecture for FL aggregation. First, we demonstrate how traditional tree overlay based aggregation techniques (from P2P, publish-subscribe and stream processing research) can help FL aggregation scale, but are ineffective from a resource utilization and cost standpoint. Next, we present the design and implementation of AdaFed, which uses serverless/cloud functions to adaptively scale aggregation in a resource efficient and fault tolerant manner. We describe how AdaFed enables FL aggregation to be dynamically deployed only when necessary, elastically scaled to handle participant joins/leaves and is fault tolerant with minimal effort required on the (aggregation) programmer side. We also demonstrate that our prototype based on Ray scales to thousands of participants, and is able to achieve a >90% reduction in resource requirements and cost, with minimal impact on aggregation latency.