Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.17546

FLAI Protocol: Decentralized Federated Learning with On-Chain Rewards and sPEG-Based Contribution Scoring

Eli Yune·Mahesh Balan
View original source ↗Read source document ↗Export metadata ↓

Abstract

Federated learning (FL) enables collaborative model training without data centralization but struggles with incentive alignment, accountability, and robustness under heterogeneous non-IID data. Existing decentralized FL (DeFL) systems leverage blockchains for coordination yet face high on-chain overhead, unverifiable self-reports, and weak fairness guarantees. We present the FLAI Protocol, a trusted execution environment (TEE)-backed DeFL framework that integrates enclave-validated aggregation, sparse Peer Evaluation Graph (sPEG) scoring, and on-chain settlement. sPEG incorporates peer-based quality, class coverage, promptness, effort, and reputation into composite contribution scores. The protocol formalizes contribution evaluation and tokenized incentive distribution, ensuring verifiable fairness and resilience against free-riding and collusion. We evaluate FLAI on image, text, and healthcare datasets and demonstrate parity with FedAvg in utility while improving fairness and accountability. A case study in consumer eHealth (aggregating wearable and electronic health record data for real-world evidence generation) illustrates the protocol's applicability to privacy-critical domains where equitable rewards, auditability, and data sovereignty are essential.

Explore related subjects

cs.DC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Eli Yune, Mahesh Balan. 2026-10-13. FLAI Protocol: Decentralized Federated Learning with On-Chain Rewards and sPEG-Based Contribution Scoring. https://doi.org/10.1109/icce67443.2026.11449597

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

PerCache: Predictive Hierarchical Cache for RAG Applications on Mobile Devices

Retrieval-augmented generation (RAG) has become a de facto paradigm in various large language model (LLM)-driven applications on mobile devices, such as mobile assistants leveraging personal emails or meeting records. However, due to lengthy prompts and resource constraints, mobile RAG systems exhibit significantly high response latency. To address this issue, one promising approach is to reuse intermediate computational results across different queries to eliminate redundant computation. But most existing approaches, such as key-value (KV) cache reuse and semantic cache reuse, are designed for cloud settings and perform poorly on mobile devices as they overlook the distinctive characteristics of mobile RAG. We propose PerCache, a novel hierarchical cache framework designed for reducing end-to-end latency of personalized RAG applications on mobile platforms. PerCache adopts a hierarchical architecture that progressively matches similar queries and query-key-value (QKV) cache to maximize the reuse of intermediate results at different computing stages. To improve cache hit rate, PerCache applies a predictive method to populate the cache with queries that are likely to be raised in the future. In addition, PerCache can adapt its configurations to dynamic system loads, aiming to maximize caching utility with constrained resource consumption. We implement PerCache on top of an existing mobile LLM inference engine and deploy it on commodity mobile phones. Extensive evaluations show that PerCache achieves the lowest average end-to-end latency across five datasets and two LLM architectures, reducing latency by up to 63.5% over the strongest baseline while maintaining comparable answer quality.

cs.DC↗
arXiv2026

Dynamic Resource Management in Production HPC Clusters

Many large-scale scientific applications exhibit time-varying behavior, yet production HPC clusters still rely on rigid, fixed-size allocations, and most dynamic techniques remain confined to laboratory prototypes. This work presents a practical MPI malleability methodology that integrates with state-of-the-art high-performance computing (HPC) software stacks and operational practices. The methodology is implemented in the Dynamic Management of Resources (DMR) framework and is designed to ease adoption by existing applications without requiring intrusive code changes or scheduler modifications. We evaluate our approach by integrating the DMR API into two large-scale scientific applications and deploying them on three TOP500 supercomputers under realistic production configurations. Our non-invasive malleability solution achieves performance comparable to static baselines in controlled environments while substantially reducing node-hour consumption for identical workloads. These results show that malleability can be effectively exploited on production systems using vanilla resource managers, lowering the barrier to adoption of dynamic resource management in HPC.

cs.DC↗
arXiv2026

Purlin: Separating Orchestration from the Datapath of Collectives

Distributed inference depends on GPU collective communication that must keep pace with evolving hardware and specialized workloads. However, existing works often couple the semantics, orchestration (where and when data moves), and datapath (how data moves) of collective communication. This coupling makes it costly to adopt new hardware mechanisms and customize communication for applications. We present Purlin, a scale-up communication framework that separates these concerns. At the top of Purlin, we specify collectives as a naming of an input and output layout and a copy or reduction operation. In the middle, we introduce a shared orchestration protocol, Stage, Notify, And Consume (SNAC), which derives coordination from these specifications. Below SNAC sits a hardware-specific datapath we call Atom, which implements two key data movement primitives for collectives: copy and reduce. This separation lets us customize collectives and adopt new hardware mechanisms while reusing orchestration via SNAC. We evaluate Purlin on A100, H200, and B200 GPUs. Across seven collectives, Purlin achieves latency speedups of up to 5.14x and bandwidth improvements of up to 4.50x over baselines. Integrated into SGLang, Purlin improves offline LLM serving throughput and interactivity by 1.13x on average and up to 1.37x over baselines. For online LLM inference, Purlin improves interactivity by 1.26x on average and up to 2.85x, with the largest gain occurring under overload. For diffusion image generation, Purlin reduces end-to-end latency by up to 1.13x.

cs.DC↗