Federated and Decentralized Architectures

• Federated and decentralized architectures are distributed paradigms that preserve data privacy by keeping data local and aggregating insights through coordinated or peer-to-peer methods.
• They employ techniques such as federated averaging, secure multiparty computation, and blockchain protocols to address challenges in scalability, security, and non-IID data across diverse domains.
• These architectures enhance resilience and trust by eliminating single points of failure while raising considerations in coordination, communication overhead, and standardization.

Federated and decentralized architectures describe complementary paradigms for the design and operation of distributed systems, with increasing relevance in privacy-preserving analytics, collaborative machine learning, personal data management, peer-to-peer communication, and scientific data sharing. These architectures have evolved in response to the limitations of traditional centralized models, offering variants in trust, coordination, scalability, and resilience. Federated architectures typify collaborative ecosystems of multiple coordinated but independently operated nodes; decentralized architectures eliminate or minimize centralized roles entirely, often distributing coordination, aggregation, and governance across peer nodes or dynamic subgroups.

1. Historical and Conceptual Evolution

The trajectory from early distributed computing to modern federated and fully decentralized architectures is marked by technical, social, and economic drivers. Initial proposals, such as negotiated privacy techniques and commercial infomediaries, emerged in the late 1990s as a response to aggregating power and user data in centralized web platforms (Narayanan et al., 2012). Subsequent patterns included personal data stores, VRM systems, and federated social networks. In parallel, federated and decentralized learning emerged to reconcile the scalability and analytics of machine learning with imperatives for local data privacy and domain control (Nasim et al., 7 Feb 2025, Beltrán et al., 2022).

The federated learning paradigm was popularized as scalable alternatives to centralized deep learning, simultaneously tackling regulatory and technical challenges in domains from medical (Beltrán et al., 2022) and IoT (Mukherjee et al., 6 Aug 2024) applications to energy management (Zekiye et al., 2023). Decentralized architectures—often but not always synonymous with peer-to-peer or blockchain-based models—aimed to eliminate central points of control and failure, bringing both scalability and distinct hardness in coordination, trust bootstrap, and attack resistance (Halpin, 2020).

2. Architectural Patterns and Roles

Architectural taxonomy is rooted in differing operational loci and trust models:

Paradigm	Coordination Locus	Aggregation Model
Centralized	Single server entity	Server-driven
Federated	Multiple trusted nodes	Cross-node (server-server or serverless)
Decentralized	Peer-to-peer, dynamic	Serverless, local or group

• Federated architectures: Aggregate across independently managed organizations or servers (e.g., federated social media, cross-silo federated learning). Example: in social networks like Mastodon, each "instance" is autonomous, interconnected via standard protocols (ActivityPub), supporting profile replication, content delivery, and user interaction spanning local and remote hosts (Jeong et al., 31 Mar 2025).
• Decentralized architectures: Distribute aggregation, learning, and consensus directly among peers, eschewing any single point of orchestration. Examples include blockchain-based federated learning (Ghanem et al., 2021, Zekiye et al., 2023, Cassano et al., 9 Jul 2024), aggregate-by-gossip machine learning protocols (Hu et al., 2019), and data mesh/data ecosystem frameworks that federate domain-specific storage and computation (Li et al., 26 Mar 2024, Beyvers et al., 28 Apr 2025).
• Hybrid forms: Dynamic or hierarchical architectures further blend aggregation among clusters or assign role rotation, often optimizing for latency, scalability, or regulatory boundaries (Nasim et al., 7 Feb 2025, Beltrán et al., 2022).

Distinct functional roles emerge: trainers (workers), validators (aggregators/checkers), governance nodes (policy enforcement), and coordination layers (e.g., smart contracts or blockchain registries) (Ghanem et al., 2021, Cassano et al., 9 Jul 2024, Yagiz et al., 24 Aug 2025).

3. Technical Differentiators and Methodologies

Federated and decentralized architectures are distinguished along multiple technical axes:

• Data locality and control: Data remains on client devices, domains, or local storage units, never transferred in raw form (Nasim et al., 7 Feb 2025, Beltrán et al., 2022, Li et al., 26 Mar 2024). This both preserves privacy and complicates joint computation.
• Aggregation and consensus: Federated averaging (FedAvg) and related strategies coordinate parameter or gradient exchange and aggregation, classically centralized but also implemented in decentralized forms using peer-to-peer topologies or blockchain-driven smart contracts (Sun et al., 2021, Hu et al., 2019, Ghanem et al., 2021).
• Communication patterns: Fashioned as synchronous or asynchronous message-passing, adaptations include segmented gossip (splitting models and parallelizing transfer) (Hu et al., 2019), ring/mesh collective aggregation (Mukherjee et al., 6 Aug 2024), hierarchical communication (clusters with local/global aggregators) (Beltrán et al., 2022), and blockchain-based message validation and audit (Zekiye et al., 2023, Ghanem et al., 2021, Cassano et al., 9 Jul 2024).
• Privacy-preserving schemes: Homomorphic encryption, secure multiparty computation, differential privacy, and trusted execution/validation environments are leveraged to prevent model or membership inference (Cassano et al., 9 Jul 2024, Beltrán et al., 2022, Nasim et al., 7 Feb 2025).
• Topology adaptation and optimization: Mechanisms range from personalized, similarity-weighted neighbor selection (Ye et al., 15 Oct 2024), topology reconfiguration based on subgraph semantics in graph learning (Guo et al., 15 Aug 2025), to dynamic reinforcement learning-based client scheduling in energy- and carbon-aware setups (Yagiz et al., 24 Aug 2025).
• Data/model heterogeneity: Advanced aggregation (e.g., FedProx), dynamic weighting, and architectural partitioning (horizontal/vertical/transfer FL) mitigate non-iid data effects and resource constraints (Beltrán et al., 2022, Nasim et al., 7 Feb 2025, Li et al., 26 Mar 2024).

4. Performance, Security, and Scaling Considerations

Empirical and analytical results illustrate both the strengths and bottlenecks of non-centralized architectures:

• Efficiency and scalability: Decentralized approaches such as segmented gossip reduce overall training duration by up to 3× compared to centralized baselines, saturating node-to-node bandwidth as the number of segments (S) increases and benefiting especially from parallelization in heterogeneous networks (Hu et al., 2019). Hierarchical or blockchain-based designs achieve horizontal scalability, with overall capacity scaling with the number of participant nodes: $\mathtt{Capacity} ∝ n$ (Beyvers et al., 28 Apr 2025).
• Robustness and privacy: Removing a single aggregation server increases resistance to targeted attacks. Systems like DFedAvgM (with momentum and quantization) maintain convergence rates and preserve privacy more effectively than centralized alternatives (Sun et al., 2021). In decentralized federated graph learning, adaptive dual-topology mechanisms yield 3.26% accuracy improvement over nonadaptive baselines (Guo et al., 15 Aug 2025). Horizontal FL with decentralized peer aggregation exhibits superior robustness to adversarial attacks than chain-based vertical FL splitting, especially under non-IID data (Sánchez et al., 2022).
• Security mechanisms: Blockchain-backed tracking, smart contracts for update verification, and decentralized consensus protocols such as practical Byzantine fault tolerance underpin high-integrity operation (Ghanem et al., 2021, Cassano et al., 9 Jul 2024, Zekiye et al., 2023). Commitment–reveal cryptographic protocols enable robust neighbor selection and trust in fully open P2P networks (Ye et al., 15 Oct 2024).
• Communication and overhead: Fully connected decentralized topologies incur per-node communication overhead proportional to $M \cdot (N-1)$, with $M$ the model size, $N$ nodes (Beltrán et al., 2022). Segmenting models, sparse or compressed updates (Schwermer et al., 3 Apr 2024), and adaptive synchronization reduce message size and frequency.
• Scalability limitations and hard problems: Seven specific challenges for decentralization are highlighted: public key discovery, key availability, group management, metadata protection, Sybil resistance, software update dissemination, and secure resource sharing (Halpin, 2020). Decentralized systems frequently trade off strong security or availability for scalability; “super-nodes” may inadvertently reintroduce elements of centralization.

5. Application Domains and Deployment Scenarios

Federated and decentralized architectures are being adopted across diverse domains:

• Healthcare and biomedicine: DFL is used for collaborative diagnosis on distributed, privacy-sensitive medical datasets, with demonstrable improvements in regulatory compliance and robust performance (Beltrán et al., 2022).
• Industry 4.0 and IoT: Distributed machine learning on edge and sensor networks leverages decentralized approaches for anomaly detection, predictive maintenance, and cybersecurity (Beltrán et al., 2022, Mukherjee et al., 6 Aug 2024).
• Scientific data ecosystems: FAIR and federated data infrastructures link research repositories and computing services via layered, peer-to-peer frameworks, scaling with horizontal node growth and supporting adaptive metadata and semantic enrichment (Beyvers et al., 28 Apr 2025).
• Online social networks: Federated architectures empower federated instances/pods in distributed social web platforms (e.g., Mastodon), offering content resilience, user autonomy, but also novel challenges in moderation and privacy (Jeong et al., 31 Mar 2025).
• Energy management and Metaverse: Blockchain-enabled FL for decentralized energy systems supports peer-to-peer trading and grid optimization; in the Metaverse context, multi-agent reinforcement learning orchestrates resource and carbon-aware client scheduling (Zekiye et al., 2023, Yagiz et al., 24 Aug 2025).

6. Societal, Economic, and Governance Implications

Architectural decisions reflect and require fundamentally different trust and governance models:

• Trust distribution: Federated systems disperse trust among known entities (e.g., server administrators, organizations); decentralized (often P2P or blockchain-based) systems aim for “trustless” operation, imposing increased cognitive and key-management burden on end-users (Halpin, 2020, Narayanan et al., 2012).
• Moderation and governance: In federated social networks, moderation is local to each server; policies can diverge, leading to “moderation shopping” and uneven data protection (Jeong et al., 31 Mar 2025). In federated research ecosystems, governance clusters and standardized machine-readable agreements (MOUs/DUAs) codify permissions and policy (Beyvers et al., 28 Apr 2025).
• Data sovereignty and autonomy: Decentralized and federated data systems allow ownership and compliance at the domain or organizational boundary, integrating digital trust mechanisms such as federated identity and permissioned blockchains (Beyvers et al., 28 Apr 2025, Zekiye et al., 2023).
• Interoperability and standardization: Achieving cross-system interoperability requires commitment to open technical standards and considerable “glue” logic development (Narayanan et al., 2012). Proliferation of overlapping standards (OpenID, ActivityPub, etc.) complicates seamless collaboration (Narayanan et al., 2012, Jeong et al., 31 Mar 2025).

7. Open Challenges and Research Directions

Several persistent open problems and future research avenues are identified across the literature:

• Scalability and resilience: Addressing communication/computation bottlenecks in massive-scale, heterogeneous networks; developing asynchrony- and outage-tolerant protocols (Beltrán et al., 2022).
• Non-IID data and personalization: Designing aggregation and learning algorithms robust to extreme data distribution heterogeneity, possibly incorporating federated neural architecture search or meta-learning (Nasim et al., 7 Feb 2025, Yuan et al., 2020).
• Security and adversarial robustness: Developing robust aggregation, attack detection, and formal guarantees against poisoning and inference attacks (Beltrán et al., 2022, Sánchez et al., 2022).
• Dynamic topologies and incentives: Enabling self-organizing network topologies, dynamic neighbor selection, and incentive alignment for cooperative behavior in open decentralized settings (Ye et al., 15 Oct 2024, Guo et al., 15 Aug 2025).
• Benchmarking and standardization: There remains a need for standardized performance benchmarks, formal taxonomies, and simulation-to-deployment frameworks that reflect real-world constraints in federated/decentralized computing (Beltrán et al., 2022, Schwermer et al., 3 Apr 2024).

The consensus across the cited works is that federated and decentralized architectures offer substantive improvements in resilience, privacy, and scalability. However, adoption remains hindered by persistent technical, economic, cognitive, and governance challenges. Realistic assessments recommend narrowly scoped, interoperable systems that layer technical protections with regulatory frameworks and support user needs beyond raw privacy. The ongoing research builds toward increasingly robust, efficient, and practically deployable federated and decentralized systems for a broad array of data-driven applications.