# HEAL: Resilient and Self-* Hub-based Learning

arXiv: https://arxiv.org/abs/2605.27475 · HTML: https://mlsanity.com/paper/2605.27475/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Beyond spectral gap: The role of the topology in decentralized learning](https://mlsanity.com/paper/2206.03093/) (2022)
- [Epidemic Learning: Boosting Decentralized Learning with Randomized Communication](https://mlsanity.com/paper/2310.01972/) (2023)
- [Data Poisoning Attacks in Gossip Learning](https://mlsanity.com/paper/2403.06583/) (2024)
- [Towards Practical Overlay Networks for Decentralized Federated Learning](https://mlsanity.com/paper/2409.05331/) (2024)

## Built on by (most cited first, 1 of 1)

- [Stream Learning: Partition-Fair Gossip Learning Without Tokens](https://mlsanity.com/paper/2608.06946/) (2026)

## Abstract (arXiv)

Decentralized learning enhances privacy, scalability, and fault tolerance by distributing data and computation across nodes. A popular approach is Federated learning, which relies on a central aggregator, yet faces challenges such as server vulnerabilities, scalability issues, privacy risks and most importantly, the single point of failure. Alternatively Gossip Learning and Epidemic Learning offer fully decentralization through peer-to-peer exchanges of model updates, ensuring robustness and privacy, at the price of slower model convergence. In this work, we introduce a novel decentralized learning framework called HEAL. HEAL is the first cross-layer decentralized learning framework that exploits an optimized self-organizing and self-healing underlying P2P overlay combining the strengths of Federated Learning, Gossip and Epidemic Learning. Leveraging the recently proposed Elevator algorithm, HEAL promotes dynamically chosen nodes to act as aggregators. Through simulations, we demonstrate that HEAL has similar performances to that of Federated Learning in crash-free settings, while being fully decentralized and fault-tolerant. In crash and churn prone environments HEAL outperforms Gossip and Epidemic Learning.