# US 2022/0044162 A1 · Blockchain-based secure federated learning > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0044162 A1 - Document ID: US20220044162A1 - Status: Published application - Kind: A1 - Application number: 17/357,843 - Filed: 2021-06-24 - Published: 2022-02-10 - Earliest claimed: 2020-08-06 - Assignees: Fujitsu Limited - Applicants: Fujitsu Limited - Inventors: Qiong ZHANG; Paparao PALACHARLA; Tadashi IKEUCHI; Motoyoshi SEKIYA; Junichi SUGA; Toru KATAGIRI - Web page: https://patentgrep.com/patent/US20220044162A1 ## CPC - G06F 21/6245: {Protecting personal data, e.g. for financial or medical purposes} - G06N 20/20: Ensemble learning - G06F 21/604: {Tools and structures for managing or administering access control systems} - G06F 21/64: Protecting data integrity, e.g. using checksums, certificates or signatures - G06N 3/08: Learning methods - G06N 3/09: Supervised learning - G06N 3/098: Distributed learning, e.g. federated learning - G06N 20/00: Machine learning - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - H04L 9/0618: {Block ciphers, i.e. encrypting groups of characters of a plain text message using fixed encryption transformation} - H04L 9/30: Public key, i.e. encryption algorithm being computationally infeasible to invert or user's encryption keys not requiring secrecy - H04L 63/126: {the source of the received data} - H04L 9/50: {using hash chains, e.g. blockchains or hash trees} - H04L 2209/04: Masking or blinding - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) ## Abstract A method may include publishing metadata to a blockchain, the metadata describing a training task associated with a global machine-learning model and computational resource requirements for performing the training task. The method may include receiving a request to participate in training the global machine-learning model from one or more clients based on a relevance of a respective local dataset of each of the clients to the training task and a suitability of the clients to the computational resource requirements for performing the training task. The method may include obtaining local model updates in which each respective local model corresponds to a respective client and each respective local model update is generated based on training the global machine-learning model with each of the local datasets. The method may include aggregating the plurality of local model updates and generating an updated global machine-learning model based on the aggregated local model update. ## Claims ### Claim 1 A method, comprising: publishing metadata to a blockchain, the metadata describing a training task associated with a global machine-learning model and one or more computational resource requirements for performing the training task; receiving a request to participate in training the global machine-learning model from each client of a plurality of clients in which each request is made by a respective client that includes a respective local dataset based on a self-analysis, by the respective client, of a relevance of the respective local dataset to the training task and a suitability of the respective client to the computational resource requirements for performing the training task; obtaining a plurality of local model updates for the global machine-learning model in which each respective local model update of the plurality of local model updates corresponds to a respective client of the plurality of clients and each respective local model update is generated based on training, by the respective client, of the global machine-learning model with a local dataset associated with the respective client; aggregating the plurality of local model updates to obtain an aggregated local model update; and generating an updated global machine-learning model based on the aggregated local model update. ### Claim 2 (depends on 1) The method of claim 1, further comprising: determining a training criterion for generating the updated global machine-learning model, the training criterion indicating a threshold number of local model updates and a threshold training accuracy of the local model updates, wherein aggregating the plurality of local model updates includes aggregating the obtained local model updates until the training criterion is satisfied. ### Claim 3 (depends on 2) The method of claim 2, wherein generating the updated global machine-learning model based on the aggregated local model update includes: calculating a weighted average of the local model updates included in the aggregated local model update, wherein: the updated global machine-learning model is generated based on the weighted average of the local model updates. ### Claim 4 (depends on 1) The method of claim 1, further comprising: publishing updated metadata associated with the updated global machine-learning model to the blockchain, the updated metadata including an updated training task identifier related to the updated global machine-learning model and one or more computational resource requirements for performing an updated training task associated with the updated training task identifier; receiving a request to participate in training the updated global machine-learning model from each client of the plurality of clients based on a self-analysis, by the respective client, of a relevance of the respective local dataset to the updated training task and a suitability of the respective client to the computational resource requirements for performing the updated training task; obtaining a plurality of local model updates for the updated global machine-learning model in which each respective local model update of the plurality of local model updates corresponds to a respective client of the plurality of clients and each respective local model update is generated based on training, by the respective client, of the updated global machine-learning model with a local dataset associated with the respective client; and determining a second updated global machine-learning model based on an aggregation of the plurality of local model updates for the updated global machine-learning model. ### Claim 5 (depends on 1) The method of claim 1, wherein the metadata include at least one of: a task identifier, a training-round identifier, a client identifier, a number of training samples, a training accuracy, a testing accuracy, a file name, or a required number of clients at each training round. ### Claim 6 (depends on 1) The method of claim 1, wherein each local model update of the plurality of local model updates comprises one or more parameters of the global machine-learning model including at least one of: a training weight, a support vector, or a coefficient. ### Claim 7 (depends on 1) The method of claim 1, wherein the global machine-learning model is trained to perform at least one of: hardware fault-prediction, network-performance prediction, or detection of non-responsive cell sites. ### Claim 8 One or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause a system to perform operations, the operations comprising: publishing metadata to a blockchain, the metadata describing a training task associated with a global machine-learning model and one or more computational resource requirements for performing the training task; receiving a request to participate in training the global machine-learning model from each client of a plurality of clients in which each request is made by a respective client that includes a respective local dataset based on a self-analysis, by the respective client, of a relevance of the respective local dataset to the training task and a suitability of the respective client to the computational resource requirements for performing the training task; obtaining a plurality of local model updates for the global machine-learning model in which each respective local model update of the plurality of local model updates corresponds to a respective client of the plurality of clients and each respective local model update is generated based on training, by the respective client, of the global machine-learning model with a local dataset associated with the respective client; aggregating the plurality of local model updates to obtain an aggregated local model update; and generating an updated global machine-learning model based on the aggregated local model update. ### Claim 9 (depends on 8) The one or more non-transitory computer-readable storage media of claim 8, further comprising: determining a training criterion for generating the updated global machine-learning model, the training criterion indicating a threshold number of local model updates and a threshold training accuracy of the local model updates, wherein aggregating the plurality of local model updates includes aggregating the obtained local model updates until the training criterion is satisfied. ### Claim 10 (depends on 9) The one or more non-transitory computer-readable storage media of claim 9, wherein generating the updated global machine-learning model based on the aggregated local model update includes: calculating a weighted average of the local model updates included in the aggregated local model update, wherein: the updated global machine-learning model is generated based on the weighted average of the local model updates. ### Claim 11 (depends on 8) The one or more non-transitory computer-readable storage media of claim 8, further comprising: publishing updated metadata associated with the updated global machine-learning model to the blockchain, the updated metadata including an updated training task identifier related to the updated global machine-learning model and one or more computational resource requirements for performing an updated training task associated with the updated training task identifier; receiving a request to participate in training the updated global machine-learning model from each client of the plurality of clients based on a self-analysis, by the respective client, of a relevance of the respective local dataset to the updated training task and a suitability of the respective client to the computational resource requirements for performing the updated training task; obtaining a plurality of local model updates for the updated global machine-learning model in which each respective local model update of the plurality of local model updates corresponds to a respective client of the plurality of clients and each respective local model update is generated based on training, by the respective client, of the updated global machine-learning model with a local dataset associated with the respective client; and determining a second updated global machine-learning model based on an aggregation of the plurality of local model updates for the updated global machine-learning model. ### Claim 12 (depends on 8) The one or more non-transitory computer-readable storage media of claim 8, wherein the metadata include at least one of: a task identifier, a training-round identifier, a client identifier, a number of training samples, a training accuracy, a testing accuracy, a file name, or a required number of clients at each training round. ### Claim 13 (depends on 8) The one or more non-transitory computer-readable storage media of claim 8, wherein each local model update of the plurality of local model updates comprises one or more parameters of the global machine-learning model including at least one of: a training weight, a support vector, or a coefficient. ### Claim 14 (depends on 8) The one or more non-transitory computer-readable storage media of claim 8, wherein the global machine-learning model is trained to perform at least one of: hardware fault-prediction, network-performance prediction, or detection of non-responsive cell sites. ### Claim 15 A system comprising: one or more processors; and one or more non-transitory computer-readable storage media configured to store instructions that, in response to being executed, cause the system to perform operations, the operations comprising: publishing metadata to a blockchain, the metadata describing a training task associated with a global machine-learning model and one or more computational resource requirements for performing the training task; receiving a request to participate in training the global machine-learning model from each client of a plurality of clients in which each request is made by a respective client that includes a respective local dataset based on a self-analysis, by the respective client, of a relevance of the respective local dataset to the training task and a suitability of the respective client to the computational resource requirements for performing the training task; obtaining a plurality of local model updates for the global machine-learning model in which each respective local model update of the plurality of local model updates corresponds to a respective client of the plurality of clients and each respective local model update is generated based on training, by the respective client, of the global machine-learning model with a local dataset associated with the respective client; aggregating the plurality of local model updates to obtain an aggregated local model update; and generating an updated global machine-learning model based on the aggregated local model update. ### Claim 16 (depends on 15) The system of claim 15, further comprising: determining a training criterion for generating the updated global machine-learning model, the training criterion indicating a threshold number of local model updates and a threshold training accuracy of the local model updates, wherein aggregating the plurality of local model updates includes aggregating the obtained local model updates until the training criterion is satisfied. ### Claim 17 (depends on 16) The system of claim 16, wherein generating the updated global machine-learning model based on the aggregated local model update includes: calculating a weighted average of the local model updates included in the aggregated local model update, wherein: the updated global machine-learning model is generated based on the weighted average of the local model updates. ### Claim 18 (depends on 15) The system of claim 15, further comprising: publishing updated metadata associated with the updated global machine-learning model to the blockchain, the updated metadata including an updated training task identifier related to the updated global machine-learning model and one or more computational resource requirements for performing an updated training task associated with the updated training task identifier; receiving a request to participate in training the updated global machine-learning model from each client of the plurality of clients based on a self-analysis, by the respective client, of a relevance of the respective local dataset to the updated training task and a suitability of the respective client to the computational resource requirements for performing the updated training task; obtaining a plurality of local model updates for the updated global machine-learning model in which each respective local model update of the plurality of local model updates corresponds to a respective client of the plurality of clients and each respective local model update is generated based on training, by the respective client, of the updated global machine-learning model with a local dataset associated with the respective client; and determining a second updated global machine-learning model based on an aggregation of the plurality of local model updates for the updated global machine-learning model. ### Claim 19 (depends on 15) The system of claim 15, wherein the metadata include at least one of: a task identifier, a training-round identifier, a client identifier, a number of training samples, a training accuracy, a testing accuracy, a file name, or a required number of clients at each training round. ### Claim 20 (depends on 15) The system of claim 15, wherein the global machine-learning model is trained to perform at least one of: hardware fault-prediction, network-performance prediction, or detection of non-responsive cell sites.