UnifyFL: Enabling Decentralized Cross-Silo Federated Learning

• URL: http://arxiv.org/abs/2504.18916v2
• Date: Tue, 06 May 2025 03:37:38 GMT
• Title: UnifyFL: Enabling Decentralized Cross-Silo Federated Learning
• Authors: Sarang S, Druva Dhakshinamoorthy, Aditya Shiva Sharma, Yuvraj Singh Bhadauria, Siddharth Chaitra Vivek, Arihant Bansal, Arnab K. Paul,
• Abstract summary: We develop a trust-based cross-silo Federated Learning framework called UnifyFL.<n>Our evaluation on a diverse testbed shows that UnifyFL achieves a performance comparable to the ideal multilevel centralized FL.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated Learning (FL) is a decentralized machine learning (ML) paradigm in which models are trained on private data across several devices called clients and combined at a single node called an aggregator rather than aggregating the data itself. Many organizations employ FL to have better privacy-aware ML-driven decision-making capabilities. However, organizations often operate independently rather than collaborate to enhance their FL capabilities due to the lack of an effective mechanism for collaboration. The challenge lies in balancing trust and resource efficiency. One approach relies on trusting a third-party aggregator to consolidate models from all organizations (multilevel FL), but this requires trusting an entity that may be biased or unreliable. Alternatively, organizations can bypass a third party by sharing their local models directly, which requires significant computational resources for validation. Both approaches reflect a fundamental trade-off between trust and resource constraints, with neither offering an ideal solution. In this work, we develop a trust-based cross-silo FL framework called UnifyFL, which uses decentralized orchestration and distributed storage. UnifyFL provides flexibility to the participating organizations and presents synchronous and asynchronous modes to handle stragglers. Our evaluation on a diverse testbed shows that UnifyFL achieves a performance comparable to the ideal multilevel centralized FL while allowing trust and optimal use of resources.

Related papers

• Adaptive Dual-Weighting Framework for Federated Learning via Out-of-Distribution Detection [53.45696787935487]
  Federated Learning (FL) enables collaborative model training across large-scale distributed service nodes.<n>In real-world service-oriented deployments, data generated by heterogeneous users, devices, and application scenarios are inherently non-IID.<n>We propose FLood, a novel FL framework inspired by out-of-distribution (OOD) detection.
  arXiv  Detail & Related papers  (2026-02-01T05:54:59Z)
• Learning to Collaborate: An Orchestrated-Decentralized Framework for Peer-to-Peer LLM Federation [0.9231476323484774]
  This paper introduces KNEXA-FL, a novel framework for orchestrated decentralization.<n>It employs a non-aggregating Central Profiler/Matchmaker that formulates P2P collaboration as a contextual bandit problem.<n>Our experiments show that KNEXA-FL yields substantial gains, improving Pass@1 by approx. 50% relative to random P2P collaboration.
  arXiv  Detail & Related papers  (2026-01-23T19:18:43Z)
• Scaling Decentralized Learning with FLock [14.614054170542966]
  This paper introduces FLock, a decentralized framework for fine-tuning large language models (LLMs)<n> Integrating a blockchain-based trust layer with economic incentives, FLock replaces the central aggregator with a secure, auditable protocol for cooperation among untrusted parties.<n>Our experiments show the FLock framework defends against backdoor poisoning attacks that compromise standard FLs.
  arXiv  Detail & Related papers  (2025-07-21T08:01:43Z)
• FedPAE: Peer-Adaptive Ensemble Learning for Asynchronous and Model-Heterogeneous Federated Learning [9.084674176224109]
  Federated learning (FL) enables multiple clients with distributed data sources to collaboratively train a shared model without compromising data privacy. We introduce Federated Peer-Adaptive Ensemble Learning (FedPAE), a fully decentralized pFL algorithm that supports model heterogeneity and asynchronous learning. Our approach utilizes a peer-to-peer model sharing mechanism and ensemble selection to achieve a more refined balance between local and global information.
  arXiv  Detail & Related papers  (2024-10-17T22:47:19Z)
• A Framework for testing Federated Learning algorithms using an edge-like environment [0.0]
  Federated Learning (FL) is a machine learning paradigm in which many clients cooperatively train a single centralized model while keeping their data private and decentralized.<n>It is non-trivial to accurately evaluate the contributions of local models in global centralized model aggregation.<n>This is an example of a major challenge in FL, commonly known as data imbalance or class imbalance.<n>In this work, a framework is proposed and implemented to assess FL algorithms in a more easy and scalable way.
  arXiv  Detail & Related papers  (2024-07-17T19:52:53Z)
• AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices [61.66943750584406]
  We propose an Asynchronous Efficient Decentralized FL framework, i.e., AEDFL, in heterogeneous environments. First, we propose an asynchronous FL system model with an efficient model aggregation method for improving the FL convergence. Second, we propose a dynamic staleness-aware model update approach to achieve superior accuracy. Third, we propose an adaptive sparse training method to reduce communication and computation costs without significant accuracy degradation.
  arXiv  Detail & Related papers  (2023-12-18T05:18:17Z)
• Secure Decentralized Learning with Blockchain [13.795131629462798]
  Federated Learning (FL) is a well-known paradigm of distributed machine learning on mobile and IoT devices. To avoid the single point of failure problem in FL, decentralized learning (DFL) has been proposed to use peer-to-peer communication for model aggregation.
  arXiv  Detail & Related papers  (2023-10-10T23:45:17Z)
• zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning [13.086807746204597]
  Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator.<n>Traditional FL relies on the trust assumption of the central aggregator, which forms cohorts of clients honestly.<n>We introduce zkFL, which leverages zero-knowledge proofs to tackle the issue of a malicious aggregator during the training model aggregation process.
  arXiv  Detail & Related papers  (2023-10-04T03:24:33Z)
• A Survey on Decentralized Federated Learning [0.709016563801433]
  In recent years, federated learning has become a popular paradigm for training distributed, large-scale, and privacy-preserving machine learning (ML) systems. In a typical FL system, the central server acts only as an orchestrator; it iteratively gathers and aggregates all the local models trained by each client on its private data until convergence. One of the most critical challenges is to overcome the centralized orchestration of the classical FL client-server architecture. Decentralized FL solutions have emerged where all FL clients cooperate and communicate without a central server.
  arXiv  Detail & Related papers  (2023-08-08T22:07:15Z)
• Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training [67.67045085186797]
  Almost all existing systems have to face large communication burdens if the central FL server fails. It personalizes the "right" in the deep models by alternately updating the shared and personal parameters. To further promote the shared parameters aggregation process, we propose DFed integrating the local Sharpness Miniization.
  arXiv  Detail & Related papers  (2023-05-24T13:52:18Z)
• Reliable Federated Disentangling Network for Non-IID Domain Feature [62.73267904147804]
  In this paper, we propose a novel reliable federated disentangling network, termed RFedDis. To the best of our knowledge, our proposed RFedDis is the first work to develop an FL approach based on evidential uncertainty combined with feature disentangling. Our proposed RFedDis provides outstanding performance with a high degree of reliability as compared to other state-of-the-art FL approaches.
  arXiv  Detail & Related papers  (2023-01-30T11:46:34Z)
• Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network [19.23943687834319]
  Federated learning (FL) has emerged as a promising privacy-preserving distributed machine learning framework. We propose a novel FL training framework, dubbed Fed-FSNet, using a properly designed Fuzzy Synthesizing Network (FSNet) to mitigate the Non-I.I.D. at-the-source issue.
  arXiv  Detail & Related papers  (2022-08-21T18:40:51Z)
• Federated Multi-Task Learning under a Mixture of Distributions [10.00087964926414]
  Federated Learning (FL) is a framework for on-device collaborative training of machine learning models. First efforts in FL focused on learning a single global model with good average performance across clients, but the global model may be arbitrarily bad for a given client. We study federated MTL under the flexible assumption that each local data distribution is a mixture of unknown underlying distributions.
  arXiv  Detail & Related papers  (2021-08-23T15:47:53Z)
• Multi-Center Federated Learning [62.32725938999433]
  Federated learning (FL) can protect data privacy in distributed learning. It merely collects local gradients from users without access to their data. We propose a novel multi-center aggregation mechanism.
  arXiv  Detail & Related papers  (2021-08-19T12:20:31Z)
• Federated Robustness Propagation: Sharing Adversarial Robustness in Federated Learning [98.05061014090913]
  Federated learning (FL) emerges as a popular distributed learning schema that learns from a set of participating users without requiring raw data to be shared. adversarial training (AT) provides a sound solution for centralized learning, extending its usage for FL users has imposed significant challenges. We show that existing FL techniques cannot effectively propagate adversarial robustness among non-iid users. We propose a simple yet effective propagation approach that transfers robustness through carefully designed batch-normalization statistics.
  arXiv  Detail & Related papers  (2021-06-18T15:52:33Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation [119.19061102064497]
  We propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL) In a round of the proposed BLADE-FL, each client broadcasts its trained model to other clients, competes to generate a block based on the received models, and then aggregates the models from the generated block before its local training of the next round. We explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients.
  arXiv  Detail & Related papers  (2021-01-18T07:19:08Z)
• Blockchain Assisted Decentralized Federated Learning (BLADE-FL) with Lazy Clients [124.48732110742623]
  We propose a novel framework by integrating blockchain into Federated Learning (FL) BLADE-FL has a good performance in terms of privacy preservation, tamper resistance, and effective cooperation of learning. It gives rise to a new problem of training deficiency, caused by lazy clients who plagiarize others' trained models and add artificial noises to conceal their cheating behaviors.
  arXiv  Detail & Related papers  (2020-12-02T12:18:27Z)
• F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning [110.35516334788687]
  Decentralized multi-agent reinforcement learning algorithms are sometimes unpractical in complicated applications. We propose a flexible fully decentralized actor-critic MARL framework, which can handle large-scale general cooperative multi-agent setting. Our framework can achieve scalability and stability for large-scale environment and reduce information transmission.
  arXiv  Detail & Related papers  (2020-04-17T14:56:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.